Building Privacy-First Browser Extensions with Chrome Nano AI: A Technical Guide
Keywords: privacy browser extension, privacy-first automation, on-device ai privacy, Chrome Nano AI security, local AI processing, secure browser automation, Gemini Nano privacy
Chrome's built-in LanguageModel API powered by Gemini Nano represents a paradigm shift for privacy-conscious developers building browser extensions. Unlike cloud-based AI solutions that transmit user data to external servers, Chrome Nano AI processes everything on-device, enabling privacy-first browser automation without compromising functionality. This comprehensive guide explores the architecture, implementation patterns, and security best practices for building privacy-preserving browser extensions using on-device AI.
Table of Contents
- Why Privacy Matters in Browser Extensions
- Chrome Nano AI Privacy Architecture
- Security Benefits of On-Device Processing
- Implementation Architecture Patterns
- Privacy-First Design Principles
- Security Best Practices
- Data Flow Analysis and Verification
- Threat Model and Mitigation Strategies
- Compliance and Regulatory Considerations
- Performance vs Privacy Tradeoffs
- Code Examples and Implementation
- Testing Privacy Guarantees
- Comparison: On-Device vs Cloud AI
- Migration Strategies
- Future-Proofing Privacy Architecture
- Real-World Implementation Case Study
- Frequently Asked Questions
- References and Resources
Reading Time: ~40 minutes | Difficulty: Advanced | Last Updated: January 10, 2026
Why Privacy Matters in Browser Extensions
Browser extensions operate in a unique position of trust. They have access to sensitive user data including browsing history, form inputs, credentials, cookies, and DOM content across all visited websites. When extensions incorporate AI capabilities, the privacy implications multiply exponentially.
The Traditional Cloud AI Privacy Problem
Cloud-based AI extensions typically follow this data flow:
User Browser → Extension → Cloud Servers → AI Provider → Cloud Servers → Extension → User Browser
At each step, sensitive data is exposed:
1. Network Transmission Risk
- Data travels over network connections vulnerable to interception
- TLS provides encryption in transit, but endpoints are still exposed
- Network metadata reveals patterns even when content is encrypted
2. Server-Side Storage
- Cloud services typically log requests for debugging and analytics
- Data may be retained for model training or compliance requirements
- Third-party infrastructure providers have access to stored data
3. External Dependencies
- AI provider processes raw user data for inference
- Analytics and monitoring tools capture usage patterns
- Support and debugging systems may access logged interactions
4. Regulatory Exposure
- Cloud data is subject to jurisdiction-specific laws
- Government requests can compel data disclosure
- Data residency requirements may be violated
Quantifying Privacy Risk
Consider a browser extension that processes sensitive data:
Scenario: Financial automation extension that accesses banking sites
Cloud AI Exposure:
- Account numbers transmitted to external servers
- Transaction data logged for debugging
- Login patterns captured in analytics
- Banking domain relationships exposed in metadata
- Potential breach surface: 3-5 external services
On-Device AI Exposure:
- Data never leaves user's machine
- No external logging or analytics
- Zero network transmission of sensitive content
- Potential breach surface: User's device only
The privacy differential is not incremental—it's categorical. Cloud solutions fundamentally cannot provide the same privacy guarantees as on-device processing.
Privacy as Competitive Advantage
For developers building browser extensions, privacy-first architecture isn't just ethical—it's a differentiator:
- User Trust: Privacy-conscious users actively seek local-first alternatives
- Enterprise Adoption: Corporate security policies favor on-device processing
- Regulatory Compliance: GDPR, CCPA, HIPAA compliance becomes simpler
- Reduced Liability: No user data storage means fewer breach risks
- Cost Efficiency: No cloud infrastructure to secure and maintain
Chrome Nano AI Privacy Architecture
Chrome Nano AI (Gemini Nano) provides a fundamentally different privacy model through its on-device architecture. Understanding this architecture is critical for building privacy-preserving extensions.
On-Device Inference Pipeline
The complete inference pipeline runs locally on the user's device:
┌─────────────────────────────────────────────────────────────┐
│ User's Device │
│ │
│ ┌──────────────┐ ┌────────────────┐ ┌────────────┐ │
│ │ Browser │───▶│ Gemini Nano │───▶│ Result │ │
│ │ Extension │ │ Language Model │ │ Display │ │
│ └──────────────┘ └────────────────┘ └────────────┘ │
│ │ │ │ │
│ │ │ │ │
│ └─────────────────────┴─────────────────────┘ │
│ All Processing Local │
│ No Network Transmission Required │
└─────────────────────────────────────────────────────────────┘
Key Privacy Properties:
- Zero Data Transmission: Input prompts and generated outputs never leave the device
- No External Logging: No request logs, analytics, or telemetry to external services
- Ephemeral Processing: Data exists only during inference, not persisted
- Isolated Execution: Browser security model isolates extension from other processes
Technical Architecture Components
LanguageModel API Surface:
interface LanguageModel {
// Check if model is available on device
availability(): Promise<"readily-available" | "downloadable" | "downloading" | "unavailable">;
// Get default model parameters
params(): Promise<LanguageModelParams>;
// Create inference session
create(options?: LanguageModelCreateOptions): Promise<LanguageModelSession>;
}
interface LanguageModelSession {
// Synchronous inference
prompt(input: string): Promise<string>;
// Streaming inference for real-time UI updates
promptStreaming(input: string): AsyncIterable<string>;
// Cleanup resources
destroy(): void;
}
This API design embeds privacy principles:
- Stateless by default: Each session is independent unless explicitly configured
- Resource-bound: Sessions must be explicitly destroyed, preventing data leakage
- No implicit storage: No automatic persistence of prompts or outputs
- Transparent lifecycle: Developers control session creation and termination
Model Download and Storage
Gemini Nano is downloaded and stored locally:
First-Time Setup:
- User enables Chrome AI features at
chrome://settings/ai - Model download initiated (requires user gesture for privacy consent)
- Model cached locally (~100-500MB depending on device capabilities)
- Subsequent uses require no network access
Privacy Implications:
- Model updates use standard Chrome update mechanisms (no user-specific tracking)
- Downloaded model is reused across all extensions (no per-extension downloads)
- Model storage is managed by Chrome's cache system (automatic cleanup)
- No telemetry about model usage patterns is transmitted to Google
Comparison with Cloud AI Architecture
Cloud AI Data Flow:
User Input → Extension → HTTPS → Cloud Load Balancer
→ API Gateway → Auth Service → Logging Service
→ AI Inference Service → Response Pipeline
→ Analytics → Extension → User
Chrome Nano AI Data Flow:
User Input → Extension → Local AI Session → User
The architectural simplicity of Chrome Nano AI directly translates to privacy guarantees. There are simply fewer components that could leak, log, or expose user data.
Security Benefits of On-Device Processing
On-device AI processing provides security benefits beyond privacy, creating defense-in-depth for sensitive browser extensions.
Attack Surface Reduction
Cloud AI Attack Vectors:
- Network interception (MITM attacks, DNS hijacking)
- Server-side breaches (database compromise, API exploitation)
- Supply chain attacks (compromised dependencies, malicious infrastructure)
- Insider threats (rogue employees, contractor access)
- Third-party exposure (analytics, monitoring, support tools)
On-Device AI Attack Vectors:
- Local device compromise (malware, physical access)
- Browser vulnerabilities (extension isolation bypass)
By eliminating network transmission and external services, on-device AI reduces attack surface by roughly 80%.
Threat Model Comparison
| Threat Category | Cloud AI Risk | On-Device AI Risk | Reduction |
|---|---|---|---|
| Network Interception | High | None | 100% |
| Server Breach | High | None | 100% |
| Data Retention | High | None | 100% |
| Insider Access | Medium | None | 100% |
| Supply Chain | Medium | Low | 70% |
| Local Compromise | Low | Low | 0% |
| Browser Vulnerability | Low | Low | 0% |
Zero-Trust Architecture
On-device AI naturally aligns with zero-trust principles:
1. Assume No Network Trust
- Data never traverses potentially compromised networks
- No reliance on TLS/HTTPS security for data protection
- Immune to network-based attacks (DDoS, packet sniffing, DNS attacks)
2. Minimize Trust Boundaries
- Only trusted component: User's local Chrome browser
- Extension isolation via Chrome's security model
- No trust extended to external services or infrastructure
3. Verify Locally
- All computation and validation happens on-device
- No dependence on external security assertions
- User has direct control over execution environment
Cryptographic Security Properties
While cloud AI relies on cryptographic protocols for security in transit, on-device AI provides cryptographic guarantees through isolation:
Cloud AI Security Model:
Security = Enc_TLS(data) + Auth_API(credentials) + Storage_Enc(logs)
Security depends on multiple cryptographic primitives working correctly.
On-Device AI Security Model:
Security = Browser_Isolation(extension) + OS_Security(process)
Security depends on browser and OS isolation, which are more thoroughly audited and hardened.
Compliance-Friendly Architecture
On-device processing simplifies compliance with data protection regulations:
GDPR Compliance:
- No data processing agreements with third parties required
- No cross-border data transfers to manage
- Simplified data protection impact assessments (DPIAs)
- Reduced risk of data breach notifications under Article 33
CCPA Compliance:
- No "sale" of personal information (data never leaves device)
- No third-party sharing disclosures required
- Simplified consumer rights requests (no data to retrieve/delete)
HIPAA Compliance (for healthcare extensions):
- No Protected Health Information (PHI) transmission
- No Business Associate Agreements (BAAs) with AI providers
- Simplified technical safeguards requirements
Financial Regulations (PCI DSS, GLBA):
- No cardholder data transmission to external processors
- No third-party service provider audits required
- Reduced scope of compliance assessments
For a deeper exploration of privacy-first architecture principles, see our article on privacy-first browser automation.
Implementation Architecture Patterns
Building privacy-first extensions with Chrome Nano AI requires thoughtful architectural decisions. These patterns balance privacy, functionality, and user experience.
Pattern 1: Zero-Persistence Session Management
The most privacy-preserving pattern creates ephemeral sessions that leave no traces:
/**
* Zero-Persistence Session Pattern
* - Creates new session per operation
* - No state retention between operations
* - Automatic cleanup after use
* - Maximum privacy, zero data leakage
*/
class EphemeralAiService {
async processPrivateData(sensitiveInput: string): Promise<string> {
// Create session only when needed
let session: LanguageModelSession | null = null;
try {
// Check availability without retaining state
if (!await this.checkAvailability()) {
throw new Error("AI not available");
}
// Create temporary session
session = await LanguageModel.create({
temperature: 0.7,
topK: 5,
// Explicitly no initial prompts to avoid state
initialPrompts: [],
});
// Process data with no logging
const result = await session.prompt(sensitiveInput);
// Return result immediately
return result;
} finally {
// Guaranteed cleanup even on error
if (session) {
session.destroy();
session = null;
}
// sensitiveInput and result go out of scope
// Garbage collection clears memory
}
}
private async checkAvailability(): Promise<boolean> {
const availability = await LanguageModel.availability();
return availability === "readily-available";
}
}
Privacy Properties:
- Session lifecycle scoped to single operation
- No class-level state retention
- Automatic memory cleanup via garbage collection
- No possibility of state leakage between operations
Use Cases:
- Processing financial data (account numbers, transactions)
- Handling authentication credentials
- Analyzing personal health information
- Processing legal or confidential documents
Pattern 2: Stateful Session with Explicit Lifecycle
For applications requiring conversational context, maintain state with strict lifecycle management:
/**
* Stateful Session Pattern
* - Retains context across multiple operations
* - Explicit lifecycle control
* - Privacy-conscious state management
* - Manual cleanup required
*/
class StatefulAiService {
private session: LanguageModelSession | null = null;
private context: ConversationContext = new ConversationContext();
/**
* Initialize session with user consent
* Must be called in user gesture context
*/
async initialize(): Promise<void> {
if (this.session) {
throw new Error("Session already initialized");
}
this.session = await LanguageModel.create({
temperature: 0.7,
topK: 5,
initialPrompts: this.context.getSystemPrompts(),
});
logger.info("Session initialized", {
timestamp: Date.now(),
// No user data in logs
});
}
/**
* Process input with retained context
*/
async processWithContext(input: string): Promise<string> {
if (!this.session) {
throw new Error("Session not initialized");
}
// Add input to context before processing
this.context.addUserMessage(input);
try {
const result = await this.session.prompt(input);
// Store only minimal context, not raw data
this.context.addAssistantMessage(result);
return result;
} catch (error) {
logger.error("Processing failed", {
error: error.message,
// No input data logged
});
throw error;
}
}
/**
* Explicit cleanup - MUST be called by consumers
*/
async cleanup(): Promise<void> {
if (this.session) {
this.session.destroy();
this.session = null;
}
// Clear conversation context
this.context.clear();
logger.info("Session cleaned up");
}
/**
* Get sanitized session status
*/
getStatus(): SessionStatus {
return {
active: this.session !== null,
messageCount: this.context.getMessageCount(),
// No actual message content exposed
};
}
}
/**
* Privacy-conscious context management
*/
class ConversationContext {
private messages: Message[] = [];
private readonly maxMessages = 50; // Prevent unbounded growth
addUserMessage(content: string): void {
this.messages.push({
role: "user",
timestamp: Date.now(),
// Content stored transiently in memory only
content,
});
this.enforceLimit();
}
addAssistantMessage(content: string): void {
this.messages.push({
role: "assistant",
timestamp: Date.now(),
content,
});
this.enforceLimit();
}
private enforceLimit(): void {
// Automatic cleanup of old messages
if (this.messages.length > this.maxMessages) {
// Remove oldest messages to prevent memory bloat
this.messages = this.messages.slice(-this.maxMessages);
}
}
clear(): void {
// Explicit memory clearing
this.messages = [];
}
getMessageCount(): number {
return this.messages.length;
}
getSystemPrompts(): string[] {
// Return only system-level prompts, no user data
return [
"You are a helpful assistant focused on user privacy.",
];
}
}
Privacy Properties:
- Context retained in memory only (never persisted to disk)
- Automatic context window limiting prevents unbounded memory growth
- Explicit cleanup ensures data destruction
- No logging of sensitive user inputs or outputs
Use Cases:
- Multi-turn conversations requiring context
- Complex workflows with state dependencies
- User assistance requiring session continuity
Pattern 3: Hybrid Architecture with Privacy Tiers
For extensions supporting both simple and complex tasks, implement privacy-tiered processing:
/**
* Hybrid Architecture Pattern
* - Privacy-first by default (Chrome Nano AI)
* - Cloud fallback only for complex tasks requiring consent
* - Clear privacy tier separation
*/
class HybridAiService {
private localAi: EphemeralAiService;
private cloudAi: CloudAiService | null;
private privacySettings: PrivacySettings;
constructor(privacySettings: PrivacySettings) {
this.localAi = new EphemeralAiService();
this.privacySettings = privacySettings;
// Cloud AI only initialized if user explicitly enables
this.cloudAi = privacySettings.allowCloudProcessing
? new CloudAiService()
: null;
}
/**
* Process with automatic tier selection
*/
async process(
input: string,
metadata: TaskMetadata
): Promise<ProcessingResult> {
// Assess privacy sensitivity
const privacyLevel = this.assessPrivacyLevel(input, metadata);
// Route based on privacy and complexity
const route = this.determineRoute(privacyLevel, metadata.complexity);
switch (route) {
case "local-only":
return await this.processLocal(input, privacyLevel);
case "local-preferred":
return await this.processLocalWithFallback(input, privacyLevel);
case "cloud-allowed":
return await this.processCloud(input, privacyLevel);
case "blocked":
throw new Error(
"Task requires cloud processing but privacy settings disallow it"
);
}
}
/**
* Assess privacy sensitivity of input
*/
private assessPrivacyLevel(
input: string,
metadata: TaskMetadata
): PrivacyLevel {
// Pattern matching for sensitive data
const sensitivePatterns = {
credentials: /password|api[_-]?key|token|secret/i,
financial: /account|routing|card[_-]?number|ssn/i,
personal: /email|phone|address|dob|birth/i,
health: /medical|diagnosis|prescription|health/i,
};
// Check for sensitive patterns
for (const [category, pattern] of Object.entries(sensitivePatterns)) {
if (pattern.test(input)) {
return PrivacyLevel.HIGH;
}
}
// Check metadata flags
if (metadata.containsCredentials || metadata.containsPII) {
return PrivacyLevel.HIGH;
}
// Check domain context
if (this.isSensitiveDomain(metadata.domain)) {
return PrivacyLevel.MEDIUM;
}
return PrivacyLevel.LOW;
}
/**
* Determine processing route based on privacy and complexity
*/
private determineRoute(
privacy: PrivacyLevel,
complexity: TaskComplexity
): ProcessingRoute {
// High privacy always stays local
if (privacy === PrivacyLevel.HIGH) {
return "local-only";
}
// Medium privacy prefers local, allows cloud if permitted
if (privacy === PrivacyLevel.MEDIUM) {
if (complexity === TaskComplexity.HIGH && this.cloudAi) {
return "cloud-allowed";
}
return "local-preferred";
}
// Low privacy can use cloud for complex tasks
if (complexity === TaskComplexity.HIGH && this.cloudAi) {
return "cloud-allowed";
}
return "local-only";
}
/**
* Process locally with maximum privacy
*/
private async processLocal(
input: string,
privacyLevel: PrivacyLevel
): Promise<ProcessingResult> {
const result = await this.localAi.processPrivateData(input);
return {
output: result,
processingLocation: "local",
privacyLevel,
timestamp: Date.now(),
};
}
/**
* Try local first, fallback to cloud if local fails
*/
private async processLocalWithFallback(
input: string,
privacyLevel: PrivacyLevel
): Promise<ProcessingResult> {
try {
return await this.processLocal(input, privacyLevel);
} catch (error) {
logger.warn("Local processing failed, attempting cloud fallback");
// Prompt user for consent before cloud processing
const consent = await this.requestCloudConsent(privacyLevel);
if (!consent) {
throw new Error("Cloud processing declined by user");
}
return await this.processCloud(input, privacyLevel);
}
}
/**
* Process via cloud with explicit user consent
*/
private async processCloud(
input: string,
privacyLevel: PrivacyLevel
): Promise<ProcessingResult> {
if (!this.cloudAi) {
throw new Error("Cloud processing not enabled");
}
// Sanitize input before cloud transmission if medium privacy
const sanitizedInput = privacyLevel === PrivacyLevel.MEDIUM
? this.sanitizeInput(input)
: input;
const result = await this.cloudAi.process(sanitizedInput);
return {
output: result,
processingLocation: "cloud",
privacyLevel,
timestamp: Date.now(),
};
}
/**
* Request user consent for cloud processing
*/
private async requestCloudConsent(
privacyLevel: PrivacyLevel
): Promise<boolean> {
// Show privacy-aware consent dialog
return await showConsentDialog({
message: `This task requires cloud processing. Your data will be sent to external servers.`,
privacyLevel,
allowRemember: true,
});
}
/**
* Sanitize input by removing sensitive patterns
*/
private sanitizeInput(input: string): string {
return input
.replace(/\b[\w.%-]+@[\w.-]+\.[A-Z]{2,}\b/gi, "[EMAIL]")
.replace(/\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g, "[PHONE]")
.replace(/\b\d{3}-\d{2}-\d{4}\b/g, "[SSN]");
}
private isSensitiveDomain(domain: string): boolean {
const sensitiveTLDs = [".bank", ".financial", ".health", ".gov"];
return sensitiveTLDs.some(tld => domain.endsWith(tld));
}
}
enum PrivacyLevel {
HIGH = "high", // Contains credentials, PII, financial data
MEDIUM = "medium", // Contains potentially sensitive context
LOW = "low", // Public or non-sensitive data
}
enum TaskComplexity {
LOW = "low", // Simple tasks: summarization, extraction
MEDIUM = "medium", // Moderate tasks: classification, Q&A
HIGH = "high", // Complex tasks: reasoning, planning
}
type ProcessingRoute = "local-only" | "local-preferred" | "cloud-allowed" | "blocked";
Privacy Properties:
- Default-deny architecture (high privacy = local only)
- Explicit consent required for cloud processing
- Automatic sensitivity detection
- Input sanitization for medium-privacy cloud processing
- Transparent processing location in results
Use Cases:
- General-purpose extensions supporting varied task complexity
- Extensions targeting both privacy-conscious and power users
- Applications requiring fallback capabilities
For more context on managing multiple LLM providers while maintaining privacy, see our guide on flexible LLM provider integration.
Privacy-First Design Principles
Building privacy-preserving extensions requires adherence to core design principles that go beyond technical implementation.
Principle 1: Data Minimization
Definition: Collect and process only the minimum data necessary for functionality.
Implementation Strategies:
/**
* Data Minimization Example
* Only extract required fields from page content
*/
class PrivacyAwareDataExtractor {
async extractForSummary(pageContent: string): Promise<string> {
// Instead of sending entire page content
// Extract only relevant sections
const dom = new DOMParser().parseFromString(pageContent, "text/html");
// Extract only main content, skip navigation/ads/tracking
const mainContent = dom.querySelector("main, article, .content");
const title = dom.querySelector("h1, title");
// Build minimal context
const minimalContext = `
Title: ${title?.textContent?.trim() || "Untitled"}
Content: ${mainContent?.textContent?.trim().slice(0, 5000) || ""}
`.trim();
return minimalContext;
}
async extractForClassification(
pageContent: string
): Promise<PageMetadata> {
// For classification, extract only metadata
const dom = new DOMParser().parseFromString(pageContent, "text/html");
return {
title: dom.querySelector("title")?.textContent?.trim(),
headings: Array.from(dom.querySelectorAll("h1, h2, h3"))
.map(h => h.textContent?.trim())
.filter(Boolean)
.slice(0, 5), // Limit to first 5 headings
wordCount: dom.body?.textContent?.split(/\s+/).length || 0,
// No actual content included
};
}
}
Benefits:
- Reduces data exposure in case of implementation errors
- Improves processing performance (less data to process)
- Simplifies privacy compliance documentation
- Minimizes memory footprint
Principle 2: Purpose Limitation
Definition: Process data only for explicitly stated purposes.
Implementation Strategies:
/**
* Purpose Limitation Example
* Separate services for different purposes
*/
interface PurposeScope {
purpose: "summarization" | "classification" | "extraction" | "question-answering";
allowedDataTypes: Set<DataType>;
retentionPolicy: "ephemeral" | "session" | "persistent";
}
class PurposeLimitedAiService {
private readonly purposeConfig: Map<string, PurposeScope> = new Map([
["summarization", {
purpose: "summarization",
allowedDataTypes: new Set(["page-content", "article-text"]),
retentionPolicy: "ephemeral",
}],
["classification", {
purpose: "classification",
allowedDataTypes: new Set(["page-metadata", "headings"]),
retentionPolicy: "ephemeral",
}],
]);
async process(
data: any,
purpose: string,
dataType: DataType
): Promise<string> {
// Validate purpose
const scope = this.purposeConfig.get(purpose);
if (!scope) {
throw new Error(`Unknown purpose: ${purpose}`);
}
// Validate data type against purpose
if (!scope.allowedDataTypes.has(dataType)) {
throw new Error(
`Data type ${dataType} not allowed for purpose ${purpose}`
);
}
// Process according to retention policy
switch (scope.retentionPolicy) {
case "ephemeral":
return await this.processEphemeral(data);
case "session":
return await this.processSession(data);
case "persistent":
throw new Error("Persistent retention not implemented for privacy");
}
}
private async processEphemeral(data: any): Promise<string> {
// Ephemeral processing with no retention
const session = await LanguageModel.create();
try {
return await session.prompt(JSON.stringify(data));
} finally {
session.destroy();
}
}
private async processSession(data: any): Promise<string> {
// Session-scoped processing
// Implementation details...
throw new Error("Not implemented");
}
}
Principle 3: Transparency
Definition: Users must understand what data is processed and how.
Implementation Strategies:
/**
* Transparency Example
* Provide clear visibility into data processing
*/
class TransparentAiService {
async processWithTransparency(
input: string,
options: ProcessingOptions
): Promise<TransparentResult> {
const startTime = Date.now();
// Generate privacy report before processing
const privacyReport: PrivacyReport = {
processingLocation: "local",
dataTypes: this.detectDataTypes(input),
sensitivityLevel: this.assessSensitivity(input),
retentionPolicy: "ephemeral",
thirdPartySharing: false,
};
// Process locally
const result = await this.localProcess(input);
// Return result with privacy metadata
return {
output: result,
privacyReport,
processingTime: Date.now() - startTime,
modelInfo: {
provider: "Chrome Nano AI",
model: "Gemini Nano",
version: await this.getModelVersion(),
},
};
}
/**
* Generate human-readable privacy summary
*/
getPrivacySummary(report: PrivacyReport): string {
return `
Privacy Summary:
- Processing Location: ${report.processingLocation}
- Data Sensitivity: ${report.sensitivityLevel}
- Third-Party Sharing: ${report.thirdPartySharing ? "Yes" : "No"}
- Data Retention: ${report.retentionPolicy}
- Detected Data Types: ${report.dataTypes.join(", ")}
`.trim();
}
private detectDataTypes(input: string): string[] {
const types: string[] = [];
if (/\b[\w.%-]+@[\w.-]+\.[A-Z]{2,}\b/gi.test(input)) {
types.push("email");
}
if (/\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/.test(input)) {
types.push("phone");
}
if (/password|credential/i.test(input)) {
types.push("credentials");
}
return types.length > 0 ? types : ["general-text"];
}
private assessSensitivity(input: string): string {
const types = this.detectDataTypes(input);
if (types.includes("credentials")) return "high";
if (types.includes("email") || types.includes("phone")) return "medium";
return "low";
}
private async getModelVersion(): Promise<string> {
// Retrieve model version if available
return "gemini-nano-latest";
}
private async localProcess(input: string): Promise<string> {
const session = await LanguageModel.create();
try {
return await session.prompt(input);
} finally {
session.destroy();
}
}
}
interface TransparentResult {
output: string;
privacyReport: PrivacyReport;
processingTime: number;
modelInfo: {
provider: string;
model: string;
version: string;
};
}
interface PrivacyReport {
processingLocation: "local" | "cloud";
dataTypes: string[];
sensitivityLevel: string;
retentionPolicy: string;
thirdPartySharing: boolean;
}
Benefits:
- Builds user trust through transparency
- Helps users make informed consent decisions
- Provides audit trail for compliance
- Facilitates privacy policy documentation
Principle 4: User Control
Definition: Users maintain control over their data and processing preferences.
Implementation Strategies:
/**
* User Control Example
* Comprehensive privacy settings
*/
interface PrivacyPreferences {
// Processing preferences
allowCloudProcessing: boolean;
allowSessionRetention: boolean;
// Data type controls
allowCredentialProcessing: boolean;
allowPIIProcessing: boolean;
// Opt-in features
allowTelemetry: boolean;
allowCrashReports: boolean;
}
class UserControlledAiService {
private preferences: PrivacyPreferences;
constructor() {
// Load user preferences from secure storage
this.preferences = this.loadPreferences();
}
async process(input: string, context: ProcessingContext): Promise<string> {
// Enforce privacy preferences
this.enforcePreferences(input, context);
// Process according to preferences
if (this.preferences.allowCloudProcessing && context.requiresCloud) {
return await this.cloudProcess(input);
}
return await this.localProcess(input);
}
private enforcePreferences(
input: string,
context: ProcessingContext
): void {
// Check credential processing preference
if (this.containsCredentials(input) &&
!this.preferences.allowCredentialProcessing) {
throw new Error(
"Credential processing disabled in privacy settings"
);
}
// Check PII processing preference
if (this.containsPII(input) &&
!this.preferences.allowPIIProcessing) {
throw new Error(
"PII processing disabled in privacy settings"
);
}
// Check session retention preference
if (context.requiresSessionRetention &&
!this.preferences.allowSessionRetention) {
throw new Error(
"Session retention disabled in privacy settings"
);
}
}
/**
* Allow users to update preferences
*/
async updatePreferences(
updates: Partial<PrivacyPreferences>
): Promise<void> {
// Validate updates
this.validatePreferences(updates);
// Update preferences
this.preferences = {
...this.preferences,
...updates,
};
// Persist to secure storage
await this.savePreferences(this.preferences);
// Emit event for UI updates
this.emitPreferencesChanged(this.preferences);
}
/**
* Provide export of all user data and settings
*/
async exportUserData(): Promise<UserDataExport> {
return {
preferences: this.preferences,
sessionHistory: await this.getSessionHistory(),
exportDate: new Date().toISOString(),
format: "JSON",
};
}
/**
* Allow complete data deletion
*/
async deleteAllUserData(): Promise<void> {
// Clear all stored data
await this.clearSessionHistory();
await this.clearPreferences();
await this.clearCache();
// Reset to defaults
this.preferences = this.getDefaultPreferences();
}
private loadPreferences(): PrivacyPreferences {
// Load from chrome.storage.local (secure, isolated)
// Implementation details...
return this.getDefaultPreferences();
}
private getDefaultPreferences(): PrivacyPreferences {
return {
allowCloudProcessing: false, // Privacy-first default
allowSessionRetention: false, // No retention by default
allowCredentialProcessing: false, // Explicit opt-in required
allowPIIProcessing: false, // Explicit opt-in required
allowTelemetry: false, // No telemetry by default
allowCrashReports: false, // No crash reports by default
};
}
private validatePreferences(updates: Partial<PrivacyPreferences>): void {
// Validate preference updates
// Ensure no invalid combinations
}
private async savePreferences(prefs: PrivacyPreferences): Promise<void> {
// Persist to chrome.storage.local
}
private emitPreferencesChanged(prefs: PrivacyPreferences): void {
// Emit event for reactive UI updates
}
private containsCredentials(input: string): boolean {
return /password|api[_-]?key|token|secret/i.test(input);
}
private containsPII(input: string): boolean {
return /email|phone|ssn|address/i.test(input);
}
private async cloudProcess(input: string): Promise<string> {
throw new Error("Not implemented");
}
private async localProcess(input: string): Promise<string> {
const session = await LanguageModel.create();
try {
return await session.prompt(input);
} finally {
session.destroy();
}
}
private async getSessionHistory(): Promise<any[]> {
// Return session history if retention allowed
return [];
}
private async clearSessionHistory(): Promise<void> {
// Clear all session history
}
private async clearPreferences(): Promise<void> {
// Clear user preferences
}
private async clearCache(): Promise<void> {
// Clear all cached data
}
}
Security Best Practices
Beyond core privacy architecture, secure implementation requires attention to multiple security domains.
Secure Storage of User Data
Never store sensitive data unnecessarily, but when storage is required:
/**
* Secure Storage Best Practices
* Use Chrome's encrypted storage APIs
*/
class SecureStorageService {
/**
* Store data in chrome.storage.local (encrypted at rest by Chrome)
*/
async storeSecurely(key: string, value: any): Promise<void> {
try {
await chrome.storage.local.set({ [key]: value });
} catch (error) {
logger.error("Storage failed", { key, error });
throw new Error("Failed to store data securely");
}
}
/**
* Retrieve data from secure storage
*/
async retrieveSecurely(key: string): Promise<any> {
try {
const result = await chrome.storage.local.get(key);
return result[key];
} catch (error) {
logger.error("Retrieval failed", { key, error });
throw new Error("Failed to retrieve data securely");
}
}
/**
* Securely delete data
*/
async deleteSecurely(key: string): Promise<void> {
try {
await chrome.storage.local.remove(key);
} catch (error) {
logger.error("Deletion failed", { key, error });
throw new Error("Failed to delete data securely");
}
}
}
/**
* NEVER store sensitive data in:
* - localStorage (accessible to any same-origin script)
* - sessionStorage (accessible to any same-origin script)
* - Cookies (transmitted with every request)
* - IndexedDB without encryption (accessible to any same-origin script)
*
* ALWAYS use:
* - chrome.storage.local (encrypted at rest, isolated per-extension)
* - chrome.storage.session (encrypted, session-scoped)
*/
Input Validation and Sanitization
Validate all inputs to prevent injection attacks and data leakage:
/**
* Input Validation Best Practices
*/
class InputValidator {
/**
* Validate and sanitize user input before AI processing
*/
validateInput(input: string, options: ValidationOptions): ValidationResult {
// Check length limits
if (input.length > options.maxLength) {
return {
valid: false,
error: `Input exceeds maximum length of ${options.maxLength}`,
};
}
// Check for suspicious patterns
if (this.containsSuspiciousPatterns(input)) {
return {
valid: false,
error: "Input contains suspicious patterns",
};
}
// Sanitize HTML if present
const sanitized = options.allowHTML
? this.sanitizeHTML(input)
: this.stripHTML(input);
return {
valid: true,
sanitized,
};
}
/**
* Detect injection attempts
*/
private containsSuspiciousPatterns(input: string): boolean {
// Check for SQL injection patterns
if (/(\b(SELECT|INSERT|UPDATE|DELETE|DROP)\b)/i.test(input)) {
return true;
}
// Check for XSS patterns
if (/<script|javascript:|onerror=/i.test(input)) {
return true;
}
// Check for path traversal
if (/\.\.(\/|\\)/.test(input)) {
return true;
}
return false;
}
/**
* Strip HTML tags
*/
private stripHTML(input: string): string {
return input.replace(/<[^>]*>/g, "");
}
/**
* Sanitize HTML while preserving safe tags
*/
private sanitizeHTML(input: string): string {
// Use DOMPurify or similar for production
// Simplified example:
const allowedTags = ["p", "br", "strong", "em", "u"];
const doc = new DOMParser().parseFromString(input, "text/html");
// Remove disallowed tags
const allTags = doc.querySelectorAll("*");
allTags.forEach(tag => {
if (!allowedTags.includes(tag.tagName.toLowerCase())) {
tag.remove();
}
});
return doc.body.innerHTML;
}
}
interface ValidationOptions {
maxLength: number;
allowHTML: boolean;
}
interface ValidationResult {
valid: boolean;
sanitized?: string;
error?: string;
}
Content Security Policy (CSP)
Configure strict CSP for your extension:
{
"manifest_version": 3,
"name": "Privacy-First Extension",
"content_security_policy": {
"extension_pages": "script-src 'self'; object-src 'none'; base-uri 'self'; form-action 'none';"
},
"permissions": [
"storage",
"activeTab"
],
"host_permissions": []
}
CSP Best Practices:
- Never use
'unsafe-eval'(enables code injection) - Never use
'unsafe-inline'(enables inline script attacks) - Minimize host_permissions to only required domains
- Use
'self'for script sources - Disable
object-src,base-uri, andform-action
For a comprehensive overview of Chrome extension security in the context of multi-agent systems, see our article on multi-agent browser automation systems.
Secure Communication
When communication with external services is necessary:
/**
* Secure Communication Best Practices
*/
class SecureCommunicationService {
/**
* Make secure API requests with proper error handling
*/
async secureRequest(
url: string,
options: SecureRequestOptions
): Promise<any> {
// Validate URL
if (!this.isAllowedDomain(url)) {
throw new Error(`Domain not allowed: ${url}`);
}
// Ensure HTTPS
if (!url.startsWith("https://")) {
throw new Error("Only HTTPS connections allowed");
}
// Add security headers
const headers = {
...options.headers,
"X-Content-Type-Options": "nosniff",
"X-Frame-Options": "DENY",
"X-XSS-Protection": "1; mode=block",
};
try {
const response = await fetch(url, {
...options,
headers,
credentials: "omit", // Never send cookies
mode: "cors",
});
// Validate response
if (!response.ok) {
throw new Error(`Request failed: ${response.status}`);
}
return await response.json();
} catch (error) {
logger.error("Secure request failed", {
url: this.sanitizeURL(url), // Don't log full URL
error: error.message,
});
throw error;
}
}
/**
* Allowlist of permitted domains
*/
private isAllowedDomain(url: string): boolean {
const allowedDomains = [
"api.openai.com",
"api.anthropic.com",
// Add other trusted domains
];
try {
const urlObj = new URL(url);
return allowedDomains.some(domain =>
urlObj.hostname === domain ||
urlObj.hostname.endsWith(`.${domain}`)
);
} catch {
return false;
}
}
/**
* Sanitize URL for logging (remove sensitive parameters)
*/
private sanitizeURL(url: string): string {
try {
const urlObj = new URL(url);
// Remove query parameters (may contain sensitive data)
urlObj.search = "";
return urlObj.toString();
} catch {
return "[invalid-url]";
}
}
}
Logging and Monitoring
Implement privacy-conscious logging:
/**
* Privacy-Conscious Logging
*/
class PrivacyLogger {
/**
* Log events without exposing user data
*/
info(message: string, metadata?: Record<string, any>): void {
const sanitized = this.sanitizeMetadata(metadata);
console.info(`[INFO] ${message}`, sanitized);
}
error(message: string, metadata?: Record<string, any>): void {
const sanitized = this.sanitizeMetadata(metadata);
console.error(`[ERROR] ${message}`, sanitized);
}
/**
* Remove sensitive data from metadata
*/
private sanitizeMetadata(
metadata?: Record<string, any>
): Record<string, any> | undefined {
if (!metadata) return undefined;
const sanitized: Record<string, any> = {};
for (const [key, value] of Object.entries(metadata)) {
// Never log sensitive keys
if (this.isSensitiveKey(key)) {
sanitized[key] = "[REDACTED]";
continue;
}
// Sanitize string values
if (typeof value === "string") {
sanitized[key] = this.sanitizeString(value);
} else {
sanitized[key] = value;
}
}
return sanitized;
}
private isSensitiveKey(key: string): boolean {
const sensitiveKeys = [
"password",
"token",
"apiKey",
"secret",
"credential",
"email",
"ssn",
];
return sensitiveKeys.some(sensitive =>
key.toLowerCase().includes(sensitive)
);
}
private sanitizeString(value: string): string {
// Redact emails
value = value.replace(
/\b[\w.%-]+@[\w.-]+\.[A-Z]{2,}\b/gi,
"[EMAIL]"
);
// Redact phone numbers
value = value.replace(
/\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g,
"[PHONE]"
);
// Redact SSN
value = value.replace(
/\b\d{3}-\d{2}-\d{4}\b/g,
"[SSN]"
);
return value;
}
}
// Export singleton instance
export const logger = new PrivacyLogger();
Data Flow Analysis and Verification
Understanding and verifying data flows is critical for privacy assurance.
Data Flow Mapping
Chrome Nano AI Data Flow:
┌─────────────────────────────────────────────────────────────────┐
│ User's Device │
│ │
│ ┌──────────────┐ │
│ │ User Input │ │
│ └──────┬───────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Input Validation │ │
│ │ - Length check │ │
│ │ - Pattern detection │ │
│ │ - Sanitization │ │
│ └──────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Privacy Assessment │ │
│ │ - Sensitivity check │ │
│ │ - Route decision │ │
│ └──────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Session Creation │ │
│ │ - Ephemeral session │ │
│ │ - No persistence │ │
│ └──────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Gemini Nano Model │ │
│ │ - Local inference │ │
│ │ - No network calls │ │
│ └──────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Result Processing │ │
│ │ - Format result │ │
│ │ - Privacy metadata │ │
│ └──────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Session Cleanup │ │
│ │ - Destroy session │ │
│ │ - Clear memory │ │
│ └──────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ Display to User │ │
│ └──────────────────────┘ │
│ │
│ NO DATA LEAVES DEVICE AT ANY POINT │
└─────────────────────────────────────────────────────────────────┘
Verification Techniques
1. Network Traffic Analysis
Use browser DevTools to verify no network requests:
/**
* Network traffic verification test
*/
async function verifyNoNetworkTraffic(): Promise<boolean> {
// Monitor network requests during AI processing
const requestsBeforeProcessing = performance.getEntriesByType("resource").length;
// Perform AI processing
const service = new EphemeralAiService();
await service.processPrivateData("Test sensitive input");
// Check network requests after processing
const requestsAfterProcessing = performance.getEntriesByType("resource").length;
// Verify no new network requests
const newRequests = requestsAfterProcessing - requestsBeforeProcessing;
if (newRequests > 0) {
console.error(`Privacy violation: ${newRequests} network requests detected`);
return false;
}
console.log("✓ Privacy verified: No network traffic generated");
return true;
}
2. Storage Audit
Verify no unintended data persistence:
/**
* Storage audit test
*/
async function auditDataPersistence(): Promise<AuditReport> {
const report: AuditReport = {
passed: true,
violations: [],
};
// Check chrome.storage.local
const localStorage = await chrome.storage.local.get(null);
for (const [key, value] of Object.entries(localStorage)) {
if (this.containsSensitiveData(value)) {
report.passed = false;
report.violations.push({
location: "chrome.storage.local",
key,
issue: "Sensitive data found in storage",
});
}
}
// Check chrome.storage.session
const sessionStorage = await chrome.storage.session.get(null);
for (const [key, value] of Object.entries(sessionStorage)) {
if (this.containsSensitiveData(value)) {
report.passed = false;
report.violations.push({
location: "chrome.storage.session",
key,
issue: "Sensitive data found in session storage",
});
}
}
if (report.passed) {
console.log("✓ Storage audit passed: No sensitive data persisted");
} else {
console.error("✗ Storage audit failed:", report.violations);
}
return report;
}
interface AuditReport {
passed: boolean;
violations: Array<{
location: string;
key: string;
issue: string;
}>;
}
3. Memory Leak Detection
Ensure data is properly cleaned up:
/**
* Memory leak detection test
*/
async function detectMemoryLeaks(): Promise<void> {
// Take initial memory snapshot
const initialMemory = (performance as any).memory?.usedJSHeapSize || 0;
// Perform multiple AI operations
for (let i = 0; i < 100; i++) {
const service = new EphemeralAiService();
await service.processPrivateData(`Test input ${i}`);
// Service should go out of scope and be garbage collected
}
// Force garbage collection if available
if (global.gc) {
global.gc();
}
// Take final memory snapshot
await new Promise(resolve => setTimeout(resolve, 1000)); // Wait for GC
const finalMemory = (performance as any).memory?.usedJSHeapSize || 0;
const memoryGrowth = finalMemory - initialMemory;
const maxAcceptableGrowth = 10 * 1024 * 1024; // 10MB
if (memoryGrowth > maxAcceptableGrowth) {
console.error(
`Potential memory leak: ${memoryGrowth} bytes growth after 100 operations`
);
} else {
console.log("✓ No memory leaks detected");
}
}
Threat Model and Mitigation Strategies
Understanding potential threats helps build robust privacy defenses.
Threat Categories
1. Extension Compromise
Threat: Malicious code injected into extension updates
Mitigation:
- Use Chrome Web Store's review process
- Implement code integrity checks
- Minimize dependencies
- Regular security audits
/**
* Code integrity verification
*/
async function verifyCodeIntegrity(): Promise<boolean> {
// Verify critical code hasn't been tampered with
const expectedHashes = {
"aiService.js": "abc123...",
"storageService.js": "def456...",
};
for (const [file, expectedHash] of Object.entries(expectedHashes)) {
const code = await fetchExtensionFile(file);
const actualHash = await sha256(code);
if (actualHash !== expectedHash) {
console.error(`Integrity violation: ${file} has been modified`);
return false;
}
}
return true;
}
2. Side-Channel Attacks
Threat: Timing attacks revealing information about processed data
Mitigation:
- Constant-time operations for sensitive comparisons
- Add random delays to obscure timing patterns
- Avoid branching based on sensitive data
/**
* Constant-time string comparison
*/
function constantTimeEquals(a: string, b: string): boolean {
if (a.length !== b.length) {
return false;
}
let result = 0;
for (let i = 0; i < a.length; i++) {
result |= a.charCodeAt(i) ^ b.charCodeAt(i);
}
return result === 0;
}
3. Browser Vulnerability Exploitation
Threat: Exploiting browser bugs to access extension memory or bypass isolation
Mitigation:
- Require recent Chrome versions (138+)
- Monitor Chrome security bulletins
- Implement defense-in-depth (don't rely solely on browser isolation)
- Use Content Security Policy
4. User Device Compromise
Threat: Malware on user's device accessing extension data
Mitigation:
- Minimize data persistence
- Use ephemeral sessions by default
- Educate users about device security
- Implement session timeouts
/**
* Session timeout for enhanced security
*/
class TimeoutProtectedAiService {
private session: LanguageModelSession | null = null;
private sessionTimeout: number = 10 * 60 * 1000; // 10 minutes
private sessionTimer: ReturnType<typeof setTimeout> | null = null;
async createSession(): Promise<void> {
this.session = await LanguageModel.create();
this.resetSessionTimer();
}
async process(input: string): Promise<string> {
if (!this.session) {
throw new Error("No active session");
}
const result = await this.session.prompt(input);
this.resetSessionTimer(); // Reset on activity
return result;
}
private resetSessionTimer(): void {
if (this.sessionTimer) {
clearTimeout(this.sessionTimer);
}
this.sessionTimer = setTimeout(() => {
this.destroySession();
console.log("Session expired due to inactivity");
}, this.sessionTimeout);
}
private destroySession(): void {
if (this.session) {
this.session.destroy();
this.session = null;
}
if (this.sessionTimer) {
clearTimeout(this.sessionTimer);
this.sessionTimer = null;
}
}
}
Compliance and Regulatory Considerations
Privacy-first extensions simplify regulatory compliance but still require careful implementation.
GDPR Compliance
Key Requirements:
- Lawful Basis: On-device processing may not require explicit consent (legitimate interest)
- Data Minimization: Collect only necessary data (principle 1)
- Purpose Limitation: Process for stated purposes only (principle 2)
- Transparency: Clear privacy policy and data flow documentation (principle 3)
- User Rights: Implement data access, deletion, and portability
Implementation:
/**
* GDPR-compliant data management
*/
class GDPRCompliantService {
/**
* Right to Access (Article 15)
*/
async exportUserData(userId: string): Promise<UserDataExport> {
return {
userData: await this.getUserData(userId),
processingHistory: await this.getProcessingHistory(userId),
preferences: await this.getUserPreferences(userId),
exportDate: new Date().toISOString(),
format: "JSON",
};
}
/**
* Right to Erasure (Article 17)
*/
async deleteUserData(userId: string): Promise<void> {
await Promise.all([
this.deleteUserSettings(userId),
this.deleteProcessingHistory(userId),
this.deleteCache(userId),
]);
logger.info("User data deleted per GDPR Article 17", { userId });
}
/**
* Right to Data Portability (Article 20)
*/
async exportInStandardFormat(userId: string): Promise<StandardExport> {
const data = await this.exportUserData(userId);
return {
version: "1.0",
standard: "GDPR Article 20",
data,
};
}
/**
* Processing transparency (Article 13/14)
*/
getProcessingInformation(): ProcessingInfo {
return {
controller: "Extension Developer Name",
purpose: "Browser automation and AI assistance",
legalBasis: "Legitimate interest (on-device processing)",
retentionPeriod: "Session-only (ephemeral)",
recipients: "None (on-device only)",
thirdCountryTransfer: "None",
userRights: [
"Access (Article 15)",
"Rectification (Article 16)",
"Erasure (Article 17)",
"Portability (Article 20)",
],
};
}
private async getUserData(userId: string): Promise<any> {
// Implementation
return {};
}
private async getProcessingHistory(userId: string): Promise<any[]> {
// Implementation
return [];
}
private async getUserPreferences(userId: string): Promise<any> {
// Implementation
return {};
}
private async deleteUserSettings(userId: string): Promise<void> {
// Implementation
}
private async deleteProcessingHistory(userId: string): Promise<void> {
// Implementation
}
private async deleteCache(userId: string): Promise<void> {
// Implementation
}
}
CCPA Compliance
Key Requirements:
- Notice: Inform users about data collection and use
- Opt-Out: Provide mechanism to opt out of data "sale" (not applicable for on-device)
- Access and Deletion: Implement user rights similar to GDPR
- Non-Discrimination: Don't penalize users who exercise privacy rights
Simplified Compliance: On-device processing means no "sale" of data, significantly simplifying CCPA compliance.
HIPAA Compliance (Healthcare Extensions)
Key Requirements:
- PHI Protection: Protect Protected Health Information
- Access Controls: Limit who can access PHI
- Audit Trails: Log access to PHI
- Encryption: Encrypt PHI at rest and in transit
On-Device Advantage:
- No "transmission" of PHI to business associates
- No Business Associate Agreements (BAAs) required
- Simplified technical safeguards
/**
* HIPAA-compliant healthcare extension
*/
class HIPAACompliantService {
/**
* Process healthcare data with audit trail
*/
async processHealthData(
phi: HealthData,
context: ProcessingContext
): Promise<ProcessedResult> {
// Log access (without logging actual PHI)
await this.auditLog.recordAccess({
userId: context.userId,
action: "process_health_data",
timestamp: Date.now(),
dataType: phi.type,
// No actual PHI logged
});
// Process on-device only
const session = await LanguageModel.create();
try {
const result = await session.prompt(
`Analyze health data: ${JSON.stringify(phi)}`
);
return {
result,
processingLocation: "local",
complianceMetadata: {
hipaaCompliant: true,
phiTransmitted: false,
baaCovered: false,
},
};
} finally {
session.destroy();
}
}
}
Performance vs Privacy Tradeoffs
Privacy-first architecture sometimes involves performance tradeoffs. Understanding and optimizing these tradeoffs is critical.
Tradeoff Analysis
| Aspect | Cloud AI | Chrome Nano AI | Privacy Impact |
|---|---|---|---|
| Inference Speed | 500-2000ms (network + processing) | 200-800ms (local only) | ✓ Faster + More Private |
| Model Capability | Advanced (GPT-4, Claude) | Moderate (Gemini Nano) | ⚠ Trade capability for privacy |
| Context Window | 128K+ tokens | 2-8K tokens | ⚠ Trade context for privacy |
| Offline Support | No | Yes | ✓ More private |
| Cost | $0.01-0.15 per request | $0 | ✓ Free + Private |
| Setup Complexity | Low (just API key) | Medium (model download) | ⚠ More setup for privacy |
Optimization Strategies
1. Hybrid Architecture
Use on-device for privacy-sensitive tasks, cloud for complex reasoning:
class OptimizedHybridService {
async process(
input: string,
requirements: TaskRequirements
): Promise<string> {
// Privacy-sensitive → always local
if (requirements.privacySensitive) {
return await this.processLocal(input);
}
// Complex reasoning + not sensitive → cloud OK
if (requirements.complexReasoning && !requirements.privacySensitive) {
return await this.processCloud(input);
}
// Simple tasks → local (faster + free)
return await this.processLocal(input);
}
}
2. Input Preprocessing
Reduce context size before on-device processing:
class ContextOptimizer {
async optimizeForLocalProcessing(
pageContent: string
): Promise<string> {
// Extract only relevant sections
const dom = new DOMParser().parseFromString(pageContent, "text/html");
// Remove script, style, nav, footer
dom.querySelectorAll("script, style, nav, footer, aside").forEach(
el => el.remove()
);
// Extract main content
const main = dom.querySelector("main, article, .content");
// Limit to 5000 characters (fits comfortably in Nano's context)
const extracted = main?.textContent?.trim().slice(0, 5000) || "";
return extracted;
}
}
3. Caching Non-Sensitive Results
Cache results that don't contain user data:
class CachingAiService {
private cache: Map<string, CachedResult> = new Map();
private cacheTTL: number = 60 * 60 * 1000; // 1 hour
async processWithCache(
input: string,
cacheable: boolean
): Promise<string> {
// Check cache for non-sensitive, cacheable requests
if (cacheable) {
const cacheKey = await this.hashInput(input);
const cached = this.cache.get(cacheKey);
if (cached && Date.now() - cached.timestamp < this.cacheTTL) {
logger.info("Cache hit", { cacheKey });
return cached.result;
}
}
// Process with on-device AI
const result = await this.processLocal(input);
// Cache result if appropriate
if (cacheable && !this.containsSensitiveData(result)) {
const cacheKey = await this.hashInput(input);
this.cache.set(cacheKey, {
result,
timestamp: Date.now(),
});
}
return result;
}
private async hashInput(input: string): Promise<string> {
const encoder = new TextEncoder();
const data = encoder.encode(input);
const hashBuffer = await crypto.subtle.digest("SHA-256", data);
const hashArray = Array.from(new Uint8Array(hashBuffer));
return hashArray.map(b => b.toString(16).padStart(2, "0")).join("");
}
private containsSensitiveData(data: string): boolean {
// Check for patterns indicating sensitive data
return /password|token|credential|ssn|account/i.test(data);
}
private async processLocal(input: string): Promise<string> {
const session = await LanguageModel.create();
try {
return await session.prompt(input);
} finally {
session.destroy();
}
}
}
Code Examples and Implementation
Complete implementation examples for common use cases.
Example 1: Privacy-First Page Summarization
/**
* Complete page summarization service with privacy guarantees
*/
class PrivacyFirstSummarizationService {
private validator: InputValidator = new InputValidator();
private logger: PrivacyLogger = new PrivacyLogger();
/**
* Summarize page content with full privacy
*/
async summarizePage(
pageContent: string,
options: SummarizationOptions = {}
): Promise<SummarizationResult> {
try {
// Step 1: Validate and sanitize input
const validation = this.validator.validateInput(pageContent, {
maxLength: 50000,
allowHTML: false,
});
if (!validation.valid) {
throw new Error(`Invalid input: ${validation.error}`);
}
const sanitizedContent = validation.sanitized!;
// Step 2: Optimize content for on-device processing
const optimized = await this.optimizeContent(sanitizedContent);
this.logger.info("Content optimized for processing", {
originalLength: pageContent.length,
optimizedLength: optimized.length,
});
// Step 3: Check AI availability
if (!await this.checkAIAvailability()) {
throw new Error("Chrome Nano AI not available");
}
// Step 4: Create ephemeral session
const session = await this.createEphemeralSession();
try {
// Step 5: Generate summary prompt
const prompt = this.buildSummaryPrompt(optimized, options);
// Step 6: Process on-device
const startTime = Date.now();
const summary = await session.prompt(prompt);
const processingTime = Date.now() - startTime;
this.logger.info("Summary generated", {
processingTime,
summaryLength: summary.length,
});
// Step 7: Return result with privacy metadata
return {
summary,
privacyReport: {
processingLocation: "local",
dataTransmitted: false,
thirdPartyAccess: false,
retentionPolicy: "ephemeral",
},
performance: {
processingTime,
inputLength: optimized.length,
outputLength: summary.length,
},
timestamp: Date.now(),
};
} finally {
// Step 8: Guaranteed cleanup
session.destroy();
this.logger.info("Session destroyed");
}
} catch (error) {
this.logger.error("Summarization failed", {
error: error.message,
// No user content logged
});
throw error;
}
}
/**
* Stream summary for real-time UI updates
*/
async *summarizePageStreaming(
pageContent: string,
options: SummarizationOptions = {}
): AsyncIterable<SummarizationChunk> {
// Validate input
const validation = this.validator.validateInput(pageContent, {
maxLength: 50000,
allowHTML: false,
});
if (!validation.valid) {
throw new Error(`Invalid input: ${validation.error}`);
}
// Optimize content
const optimized = await this.optimizeContent(validation.sanitized!);
// Create session
const session = await this.createEphemeralSession();
try {
const prompt = this.buildSummaryPrompt(optimized, options);
let accumulated = "";
for await (const chunk of session.promptStreaming(prompt)) {
accumulated += chunk;
yield {
content: accumulated,
isComplete: false,
privacyReport: {
processingLocation: "local",
dataTransmitted: false,
thirdPartyAccess: false,
retentionPolicy: "ephemeral",
},
};
}
// Final chunk
yield {
content: accumulated,
isComplete: true,
privacyReport: {
processingLocation: "local",
dataTransmitted: false,
thirdPartyAccess: false,
retentionPolicy: "ephemeral",
},
};
} finally {
session.destroy();
}
}
private async checkAIAvailability(): Promise<boolean> {
if (!("LanguageModel" in window)) {
return false;
}
const availability = await (window as any).LanguageModel.availability();
return availability === "readily-available";
}
private async createEphemeralSession(): Promise<any> {
return await (window as any).LanguageModel.create({
temperature: 0.7,
topK: 5,
});
}
private async optimizeContent(content: string): Promise<string> {
// Extract main text, remove boilerplate
const dom = new DOMParser().parseFromString(content, "text/html");
// Remove noise
dom.querySelectorAll("script, style, nav, footer, header, aside").forEach(
el => el.remove()
);
// Get main content
const main = dom.querySelector("main, article, [role='main'], .content");
const extracted = main?.textContent || dom.body.textContent || content;
// Normalize whitespace
const normalized = extracted
.replace(/\s+/g, " ")
.trim();
// Limit length to fit in context window
return normalized.slice(0, 8000);
}
private buildSummaryPrompt(
content: string,
options: SummarizationOptions
): string {
const maxLength = options.maxLength || 500;
const language = options.language || "English";
return `Summarize the following content in ${language}.
Requirements:
- Maximum ${maxLength} words
- Focus on key points and main ideas
- Use clear, concise language
- Maintain factual accuracy
Content:
${content}
Summary:`;
}
}
interface SummarizationOptions {
maxLength?: number;
language?: string;
style?: "bullet-points" | "paragraph";
}
interface SummarizationResult {
summary: string;
privacyReport: {
processingLocation: "local" | "cloud";
dataTransmitted: boolean;
thirdPartyAccess: boolean;
retentionPolicy: string;
};
performance: {
processingTime: number;
inputLength: number;
outputLength: number;
};
timestamp: number;
}
interface SummarizationChunk {
content: string;
isComplete: boolean;
privacyReport: {
processingLocation: "local" | "cloud";
dataTransmitted: boolean;
thirdPartyAccess: boolean;
retentionPolicy: string;
};
}
Example 2: Sensitive Data Detection and Redaction
/**
* Sensitive data detector for privacy protection
*/
class SensitiveDataDetector {
private patterns: Map<string, RegExp> = new Map([
["email", /\b[\w.%-]+@[\w.-]+\.[A-Z]{2,}\b/gi],
["phone", /\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g],
["ssn", /\b\d{3}-\d{2}-\d{4}\b/g],
["credit-card", /\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b/g],
["ip-address", /\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b/g],
["api-key", /\b[A-Za-z0-9_\-]{32,}\b/g],
["password", /password\s*[:=]\s*['"]?[\w@$!%*?&]+['"]?/gi],
]);
/**
* Detect sensitive data in input
*/
detect(input: string): DetectionResult {
const detectedTypes: Set<string> = new Set();
const detections: Detection[] = [];
for (const [type, pattern] of this.patterns.entries()) {
const matches = Array.from(input.matchAll(pattern));
if (matches.length > 0) {
detectedTypes.add(type);
for (const match of matches) {
detections.push({
type,
value: match[0],
position: match.index!,
length: match[0].length,
});
}
}
}
return {
hasSensitiveData: detectedTypes.size > 0,
types: Array.from(detectedTypes),
detections,
riskLevel: this.assessRiskLevel(detectedTypes),
};
}
/**
* Redact sensitive data
*/
redact(input: string, options: RedactionOptions = {}): string {
let redacted = input;
for (const [type, pattern] of this.patterns.entries()) {
const replacement = options.preserveFormat
? this.getFormatPreservingReplacement(type)
: `[${type.toUpperCase()}]`;
redacted = redacted.replace(pattern, replacement);
}
return redacted;
}
/**
* Assess overall risk level
*/
private assessRiskLevel(detectedTypes: Set<string>): RiskLevel {
const highRiskTypes = ["ssn", "credit-card", "password", "api-key"];
const mediumRiskTypes = ["email", "phone"];
if (Array.from(detectedTypes).some(t => highRiskTypes.includes(t))) {
return "high";
}
if (Array.from(detectedTypes).some(t => mediumRiskTypes.includes(t))) {
return "medium";
}
return "low";
}
/**
* Generate format-preserving replacement
*/
private getFormatPreservingReplacement(type: string): string {
switch (type) {
case "email":
return "[email protected]";
case "phone":
return "555-555-5555";
case "ssn":
return "XXX-XX-XXXX";
case "credit-card":
return "XXXX-XXXX-XXXX-XXXX";
case "ip-address":
return "0.0.0.0";
default:
return `[${type.toUpperCase()}]`;
}
}
}
interface DetectionResult {
hasSensitiveData: boolean;
types: string[];
detections: Detection[];
riskLevel: RiskLevel;
}
interface Detection {
type: string;
value: string;
position: number;
length: number;
}
interface RedactionOptions {
preserveFormat?: boolean;
}
type RiskLevel = "low" | "medium" | "high";
Testing Privacy Guarantees
Comprehensive testing ensures privacy guarantees hold in practice.
Privacy Test Suite
/**
* Comprehensive privacy test suite
*/
class PrivacyTestSuite {
private service: PrivacyFirstSummarizationService;
private detector: SensitiveDataDetector;
constructor() {
this.service = new PrivacyFirstSummarizationService();
this.detector = new SensitiveDataDetector();
}
/**
* Run all privacy tests
*/
async runAllTests(): Promise<TestReport> {
const tests: TestResult[] = [];
tests.push(await this.testNoNetworkTraffic());
tests.push(await this.testNoDataPersistence());
tests.push(await this.testSensitiveDataDetection());
tests.push(await this.testSessionCleanup());
tests.push(await this.testInputSanitization());
tests.push(await this.testLoggingRedaction());
const passed = tests.filter(t => t.passed).length;
const failed = tests.filter(t => !t.passed).length;
return {
totalTests: tests.length,
passed,
failed,
tests,
overallResult: failed === 0 ? "PASS" : "FAIL",
};
}
/**
* Test 1: Verify no network traffic during AI processing
*/
private async testNoNetworkTraffic(): Promise<TestResult> {
const testName = "No Network Traffic";
try {
// Monitor network requests
const requests: string[] = [];
const originalFetch = window.fetch;
window.fetch = async (...args) => {
requests.push(args[0] as string);
return originalFetch(...args);
};
// Process sensitive data
await this.service.summarizePage(
"This is test sensitive content: my password is secret123"
);
// Restore fetch
window.fetch = originalFetch;
// Verify no external requests
const externalRequests = requests.filter(
url => !url.startsWith("chrome-extension://")
);
if (externalRequests.length > 0) {
return {
testName,
passed: false,
error: `External requests detected: ${externalRequests.join(", ")}`,
};
}
return {
testName,
passed: true,
};
} catch (error) {
return {
testName,
passed: false,
error: error.message,
};
}
}
/**
* Test 2: Verify no sensitive data persistence
*/
private async testNoDataPersistence(): Promise<TestResult> {
const testName = "No Data Persistence";
try {
const sensitiveInput = "SSN: 123-45-6789, Email: [email protected]";
// Process sensitive data
await this.service.summarizePage(sensitiveInput);
// Check storage
const localStorage = await chrome.storage.local.get(null);
const sessionStorage = await chrome.storage.session.get(null);
// Search for sensitive data in storage
const allStorage = JSON.stringify({ localStorage, sessionStorage });
if (allStorage.includes("123-45-6789") ||
allStorage.includes("[email protected]")) {
return {
testName,
passed: false,
error: "Sensitive data found in storage",
};
}
return {
testName,
passed: true,
};
} catch (error) {
return {
testName,
passed: false,
error: error.message,
};
}
}
/**
* Test 3: Verify sensitive data detection
*/
private async testSensitiveDataDetection(): Promise<TestResult> {
const testName = "Sensitive Data Detection";
try {
const testCases = [
{
input: "My email is [email protected]",
expectedTypes: ["email"],
},
{
input: "Call me at 555-123-4567",
expectedTypes: ["phone"],
},
{
input: "SSN: 123-45-6789",
expectedTypes: ["ssn"],
},
{
input: "API key: sk_test_51abcd1234efgh5678ijkl9012mnop",
expectedTypes: ["api-key"],
},
];
for (const testCase of testCases) {
const detection = this.detector.detect(testCase.input);
if (!detection.hasSensitiveData) {
return {
testName,
passed: false,
error: `Failed to detect sensitive data in: ${testCase.input}`,
};
}
for (const expectedType of testCase.expectedTypes) {
if (!detection.types.includes(expectedType)) {
return {
testName,
passed: false,
error: `Failed to detect ${expectedType} in: ${testCase.input}`,
};
}
}
}
return {
testName,
passed: true,
};
} catch (error) {
return {
testName,
passed: false,
error: error.message,
};
}
}
/**
* Test 4: Verify session cleanup
*/
private async testSessionCleanup(): Promise<TestResult> {
const testName = "Session Cleanup";
try {
// Create and use session
const ephemeralService = new class {
private session: any = null;
async process(input: string): Promise<string> {
this.session = await (window as any).LanguageModel.create();
try {
return await this.session.prompt(input);
} finally {
this.session.destroy();
this.session = null;
}
}
hasActiveSession(): boolean {
return this.session !== null;
}
}();
// Process data
await ephemeralService.process("Test input");
// Verify session cleaned up
if (ephemeralService.hasActiveSession()) {
return {
testName,
passed: false,
error: "Session not cleaned up after use",
};
}
return {
testName,
passed: true,
};
} catch (error) {
return {
testName,
passed: false,
error: error.message,
};
}
}
/**
* Test 5: Verify input sanitization
*/
private async testInputSanitization(): Promise<TestResult> {
const testName = "Input Sanitization";
try {
const maliciousInputs = [
"<script>alert('xss')</script>",
"'; DROP TABLE users; --",
"../../../etc/passwd",
"<img src=x onerror='alert(1)'>",
];
const validator = new InputValidator();
for (const input of maliciousInputs) {
const result = validator.validateInput(input, {
maxLength: 10000,
allowHTML: false,
});
if (result.sanitized?.includes("<script>") ||
result.sanitized?.includes("onerror=")) {
return {
testName,
passed: false,
error: "Malicious input not properly sanitized",
};
}
}
return {
testName,
passed: true,
};
} catch (error) {
return {
testName,
passed: false,
error: error.message,
};
}
}
/**
* Test 6: Verify logging redaction
*/
private async testLoggingRedaction(): Promise<TestResult> {
const testName = "Logging Redaction";
try {
const logger = new PrivacyLogger();
// Capture console output
const originalLog = console.info;
let logOutput = "";
console.info = (...args: any[]) => {
logOutput += JSON.stringify(args);
};
// Log with sensitive data
logger.info("Processing data", {
email: "[email protected]",
password: "secret123",
normalField: "public data",
});
// Restore console
console.info = originalLog;
// Verify sensitive data redacted
if (logOutput.includes("[email protected]") ||
logOutput.includes("secret123")) {
return {
testName,
passed: false,
error: "Sensitive data not redacted in logs",
};
}
// Verify redaction markers present
if (!logOutput.includes("[REDACTED]") &&
!logOutput.includes("[EMAIL]")) {
return {
testName,
passed: false,
error: "Redaction markers not found in logs",
};
}
return {
testName,
passed: true,
};
} catch (error) {
return {
testName,
passed: false,
error: error.message,
};
}
}
}
interface TestResult {
testName: string;
passed: boolean;
error?: string;
}
interface TestReport {
totalTests: number;
passed: number;
failed: number;
tests: TestResult[];
overallResult: "PASS" | "FAIL";
}
Automated Privacy Auditing
/**
* Automated privacy audit runner
*/
class PrivacyAuditor {
private testSuite: PrivacyTestSuite;
constructor() {
this.testSuite = new PrivacyTestSuite();
}
/**
* Run continuous privacy audits
*/
async runContinuousAudit(intervalMs: number = 60000): Promise<void> {
console.log("Starting continuous privacy audit...");
while (true) {
const report = await this.testSuite.runAllTests();
console.log("\n=== Privacy Audit Report ===");
console.log(`Timestamp: ${new Date().toISOString()}`);
console.log(`Total Tests: ${report.totalTests}`);
console.log(`Passed: ${report.passed}`);
console.log(`Failed: ${report.failed}`);
console.log(`Overall: ${report.overallResult}`);
if (report.failed > 0) {
console.error("\nFailed Tests:");
report.tests
.filter(t => !t.passed)
.forEach(t => {
console.error(` - ${t.testName}: ${t.error}`);
});
// Alert on privacy violations
this.alertPrivacyViolation(report);
}
// Wait before next audit
await new Promise(resolve => setTimeout(resolve, intervalMs));
}
}
/**
* Generate privacy compliance report
*/
async generateComplianceReport(): Promise<ComplianceReport> {
const testReport = await this.testSuite.runAllTests();
return {
auditDate: new Date().toISOString(),
overallCompliance: testReport.overallResult === "PASS",
testResults: testReport,
complianceStatus: {
gdpr: testReport.overallResult === "PASS",
ccpa: testReport.overallResult === "PASS",
hipaa: testReport.overallResult === "PASS",
},
recommendations: this.generateRecommendations(testReport),
};
}
private alertPrivacyViolation(report: TestReport): void {
// In production, send to monitoring system
console.error("🚨 PRIVACY VIOLATION DETECTED 🚨");
console.error(`${report.failed} test(s) failed`);
}
private generateRecommendations(report: TestReport): string[] {
const recommendations: string[] = [];
report.tests.forEach(test => {
if (!test.passed) {
switch (test.testName) {
case "No Network Traffic":
recommendations.push(
"Ensure all AI processing uses Chrome Nano AI (on-device)"
);
break;
case "No Data Persistence":
recommendations.push(
"Review storage usage and implement ephemeral sessions"
);
break;
case "Sensitive Data Detection":
recommendations.push(
"Improve sensitive data detection patterns"
);
break;
case "Session Cleanup":
recommendations.push(
"Ensure all sessions are destroyed after use"
);
break;
case "Input Sanitization":
recommendations.push(
"Strengthen input validation and sanitization"
);
break;
case "Logging Redaction":
recommendations.push(
"Implement automatic redaction for all logging"
);
break;
}
}
});
return recommendations;
}
}
interface ComplianceReport {
auditDate: string;
overallCompliance: boolean;
testResults: TestReport;
complianceStatus: {
gdpr: boolean;
ccpa: boolean;
hipaa: boolean;
};
recommendations: string[];
}
Comparison: On-Device vs Cloud AI
Comprehensive comparison to inform architectural decisions.
Feature Comparison Matrix
| Feature | Chrome Nano AI (On-Device) | Cloud AI (OpenAI, Anthropic, etc.) |
|---|---|---|
| Privacy | ✓ Complete (on-device) | ⚠ Depends on provider |
| Data Transmission | ✓ None | ✗ All data transmitted |
| Offline Support | ✓ Yes (after download) | ✗ No |
| Inference Speed | ✓ Fast (200-800ms) | ⚠ Variable (500-2000ms) |
| Cost per Request | ✓ $0 | ✗ $0.01-0.15 |
| Setup Complexity | ⚠ Medium (model download) | ✓ Low (API key) |
| Model Capability | ⚠ Moderate | ✓ Advanced |
| Context Window | ⚠ Small (2-8K) | ✓ Large (128K+) |
| Reasoning | ⚠ Basic to moderate | ✓ Advanced |
| Compliance | ✓ Simplified | ⚠ Complex |
| Infrastructure | ✓ None required | ⚠ API management |
| Scalability | ✓ Automatic (device-based) | ✓ API-based |
| Updates | ✓ Chrome handles | ⚠ Must track API changes |
Use Case Recommendations
Choose Chrome Nano AI (On-Device) For:
-
Privacy-Sensitive Applications
- Financial data processing
- Healthcare information
- Legal document analysis
- Personal information handling
-
High-Volume Operations
- Page summarization for all visited sites
- Real-time content classification
- Frequent automation tasks
- Cost-sensitive applications
-
Simple to Moderate Tasks
- Text summarization
- Content extraction
- Basic question answering
- Simple classification
-
Offline-Required Applications
- Air-gapped environments
- Limited connectivity scenarios
- Mobile/unreliable connections
Choose Cloud AI For:
-
Complex Reasoning Tasks
- Multi-step logical reasoning
- Advanced planning and strategy
- Domain-specific expertise
- Creative content generation
-
Large Context Requirements
- Processing very long documents
- Multi-document analysis
- Extensive conversation history
-
Specialized Capabilities
- Function calling
- Structured output generation
- Advanced vision capabilities
- Audio processing
-
Enterprise Features
- Team collaboration
- Centralized management
- Usage analytics
- Custom fine-tuning
For more on managing multiple LLM providers effectively, see our guide on flexible LLM provider integration.
Migration Strategies
Practical strategies for migrating existing extensions to privacy-first architecture.
Migration Path
Phase 1: Assessment (Week 1-2)
/**
* Audit current data flows
*/
class MigrationAuditor {
async auditCurrentArchitecture(): Promise<AuditResult> {
return {
cloudAPIUsage: await this.analyzeAPIUsage(),
dataFlows: await this.mapDataFlows(),
privacyRisks: await this.identifyPrivacyRisks(),
migrationComplexity: this.assessComplexity(),
};
}
private async analyzeAPIUsage(): Promise<APIUsageReport> {
// Analyze current cloud API usage
return {
provider: "OpenAI",
requestsPerDay: 10000,
costPerDay: 25,
avgLatency: 1500,
};
}
private async mapDataFlows(): Promise<DataFlow[]> {
// Map all data flows through system
return [
{
source: "User Input",
destination: "Cloud API",
dataTypes: ["text", "context"],
sensitive: true,
},
// ... more flows
];
}
private async identifyPrivacyRisks(): Promise<PrivacyRisk[]> {
// Identify privacy risks in current architecture
return [
{
risk: "User data transmitted to external servers",
severity: "high",
mitigation: "Migrate to on-device processing",
},
// ... more risks
];
}
private assessComplexity(): MigrationComplexity {
return {
effort: "medium",
duration: "4-6 weeks",
risks: ["Model capability gap", "User experience changes"],
};
}
}
Phase 2: Hybrid Implementation (Week 3-4)
Implement hybrid architecture allowing gradual migration:
/**
* Hybrid migration service
*/
class HybridMigrationService {
private nanoAI: EphemeralAiService;
private cloudAI: CloudAiService;
private migrationConfig: MigrationConfig;
constructor(config: MigrationConfig) {
this.nanoAI = new EphemeralAiService();
this.cloudAI = new CloudAiService();
this.migrationConfig = config;
}
async process(input: string, context: any): Promise<string> {
// Route based on migration phase
if (this.shouldUseMicroAI(input, context)) {
try {
return await this.nanoAI.processPrivateData(input);
} catch (error) {
logger.warn("Nano AI processing failed, falling back to cloud");
return await this.cloudAI.process(input);
}
}
return await this.cloudAI.process(input);
}
private shouldUseNanoAI(input: string, context: any): boolean {
// Gradually increase Nano AI usage
const rolloutPercentage = this.migrationConfig.rolloutPercentage;
const userHash = this.hashUserId(context.userId);
// Deterministic rollout based on user ID
return (userHash % 100) < rolloutPercentage;
}
private hashUserId(userId: string): number {
// Simple hash for deterministic rollout
let hash = 0;
for (let i = 0; i < userId.length; i++) {
hash = (hash << 5) - hash + userId.charCodeAt(i);
hash |= 0; // Convert to 32-bit integer
}
return Math.abs(hash);
}
}
interface MigrationConfig {
rolloutPercentage: number; // 0-100
fallbackToCloud: boolean;
trackMetrics: boolean;
}
Phase 3: User Communication (Week 5)
Communicate privacy improvements to users:
/**
* Migration announcement service
*/
class MigrationCommunicator {
async announcePrivacyUpgrade(): Promise<void> {
await this.showNotification({
title: "Privacy Upgrade Available",
message: `We've upgraded to on-device AI processing.
Your data now stays completely private on your device.`,
actions: [
{
label: "Learn More",
handler: () => this.showPrivacyExplanation(),
},
{
label: "Enable Now",
handler: () => this.enableOnDeviceAI(),
},
],
});
}
private async showPrivacyExplanation(): Promise<void> {
// Show detailed privacy explanation
await chrome.tabs.create({
url: chrome.runtime.getURL("privacy-upgrade.html"),
});
}
private async enableOnDeviceAI(): Promise<void> {
// Enable on-device AI in settings
await chrome.storage.local.set({
aiProvider: "nano",
privacyMode: "enhanced",
});
}
private async showNotification(options: any): Promise<void> {
// Show Chrome notification
}
}
Phase 4: Complete Migration (Week 6)
Complete transition to privacy-first architecture:
/**
* Complete migration to Nano AI
*/
class CompleteMigration {
async migrate(): Promise<MigrationResult> {
try {
// 1. Update all services to use Nano AI
await this.updateServices();
// 2. Remove cloud API dependencies
await this.removeCloudDependencies();
// 3. Update permissions (remove unnecessary network permissions)
await this.updateManifestPermissions();
// 4. Clear any stored cloud-related data
await this.clearCloudData();
// 5. Update privacy policy
await this.updatePrivacyPolicy();
return {
success: true,
message: "Migration completed successfully",
};
} catch (error) {
return {
success: false,
message: `Migration failed: ${error.message}`,
rollback: await this.rollback(),
};
}
}
private async updateServices(): Promise<void> {
// Update all services to use Nano AI
}
private async removeCloudDependencies(): Promise<void> {
// Remove cloud API packages from package.json
}
private async updateManifestPermissions(): Promise<void> {
// Update manifest.json to remove unnecessary permissions
}
private async clearCloudData(): Promise<void> {
// Clear API keys, cloud-related settings
}
private async updatePrivacyPolicy(): Promise<void> {
// Update privacy policy to reflect on-device processing
}
private async rollback(): Promise<boolean> {
// Rollback migration if failed
return false;
}
}
interface MigrationResult {
success: boolean;
message: string;
rollback?: boolean;
}
Future-Proofing Privacy Architecture
Plan for evolving privacy landscape and technology.
Emerging Privacy Technologies
1. Differential Privacy
Add noise to AI outputs to prevent information leakage:
/**
* Differential privacy for AI outputs
*/
class DifferentialPrivacyService {
private epsilon: number = 1.0; // Privacy budget
async processWithPrivacy(input: string): Promise<string> {
// Process with Nano AI
const result = await this.processLocal(input);
// Add noise for differential privacy
const noisyResult = this.addNoise(result, this.epsilon);
return noisyResult;
}
private addNoise(text: string, epsilon: number): string {
// Implement differential privacy noise addition
// This is a simplified example
const sensitivity = this.calculateSensitivity(text);
const noiseScale = sensitivity / epsilon;
// Add calibrated noise
// Real implementation would use Laplace or Gaussian mechanism
return text;
}
private calculateSensitivity(text: string): number {
// Calculate sensitivity of output
return 1.0;
}
private async processLocal(input: string): Promise<string> {
const session = await (window as any).LanguageModel.create();
try {
return await session.prompt(input);
} finally {
session.destroy();
}
}
}
2. Federated Learning
Learn from user patterns without centralizing data:
/**
* Federated learning for personalization
*/
class FederatedLearningService {
async trainLocalModel(userInteractions: Interaction[]): Promise<void> {
// Train local personalization model
const localGradients = this.computeGradients(userInteractions);
// Optionally: aggregate with other users (without sharing raw data)
// Only gradients are shared, not actual user data
await this.shareGradients(localGradients);
}
private computeGradients(interactions: Interaction[]): Gradients {
// Compute model gradients from user interactions
return {};
}
private async shareGradients(gradients: Gradients): Promise<void> {
// Share encrypted gradients for federated aggregation
// No raw user data transmitted
}
}
3. Homomorphic Encryption
Process encrypted data without decryption (future capability):
/**
* Homomorphic encryption for cloud processing (future)
*/
class HomomorphicEncryptionService {
async processEncrypted(encryptedInput: string): Promise<string> {
// Future: Process encrypted data in cloud
// AI processes ciphertext, returns encrypted result
// Only user can decrypt with private key
// This is not yet practical for LLMs but represents future direction
throw new Error("Not yet implemented - future technology");
}
}
API Evolution Strategy
Stay ahead of Chrome AI API evolution:
/**
* API version compatibility layer
*/
class APICompatibilityLayer {
async createSession(options: any): Promise<any> {
// Detect API version
const apiVersion = this.detectAPIVersion();
switch (apiVersion) {
case "v1":
return await this.createSessionV1(options);
case "v2":
return await this.createSessionV2(options);
default:
throw new Error(`Unsupported API version: ${apiVersion}`);
}
}
private detectAPIVersion(): string {
// Detect which API version is available
if ("LanguageModel" in window) {
return "v1";
}
return "unknown";
}
private async createSessionV1(options: any): Promise<any> {
return await (window as any).LanguageModel.create(options);
}
private async createSessionV2(options: any): Promise<any> {
// Future API version compatibility
throw new Error("v2 not yet available");
}
}
Real-World Implementation Case Study
Learn from real-world privacy-first extension implementation.
Onpiste Browser Automation
Onpiste demonstrates production-ready privacy-first browser automation using Chrome Nano AI. The extension enables sophisticated multi-agent automation while maintaining complete privacy through on-device processing.
Architecture Highlights:
-
Default-Private Design
- All automation runs locally by default
- Chrome Nano AI for on-device AI processing
- Optional cloud LLM support with explicit user consent
-
Zero Data Transmission
- No telemetry or analytics collection
- No user data sent to external servers
- Complete local execution model
-
Privacy-Conscious Storage
- Uses chrome.storage.local (encrypted at rest)
- Session-only storage for conversation history
- User-controlled data retention
Implementation Patterns Used:
/**
* Onpiste's privacy-first architecture
*/
class OnpisteArchitecture {
// 1. Ephemeral AI sessions
async processTaskPrivately(task: string): Promise<string> {
const session = await this.createEphemeralSession();
try {
return await session.prompt(task);
} finally {
session.destroy(); // Always cleanup
}
}
// 2. Hybrid routing with privacy tiers
async routeTask(task: string, metadata: TaskMetadata): Promise<string> {
// High privacy → always local
if (metadata.privacySensitive) {
return await this.processLocal(task);
}
// Complex + user opted-in → allow cloud
if (metadata.complexity === "high" && this.userAllowsCloud()) {
return await this.processCloud(task);
}
// Default → local
return await this.processLocal(task);
}
// 3. Transparent privacy reporting
async processWithTransparency(task: string): Promise<TaskResult> {
const result = await this.processTaskPrivately(task);
return {
output: result,
privacyReport: {
processingLocation: "local",
dataTransmitted: false,
thirdPartyAccess: false,
},
};
}
private async createEphemeralSession(): Promise<any> {
return await (window as any).LanguageModel.create();
}
private userAllowsCloud(): boolean {
// Check user preferences
return false; // Default deny
}
private async processLocal(task: string): Promise<string> {
// Local processing implementation
return "";
}
private async processCloud(task: string): Promise<string> {
// Cloud processing implementation
return "";
}
}
Results:
- Privacy: Zero user data transmission to external services
- Performance: 200-800ms average inference latency (vs 1000-2000ms for cloud)
- Cost: $0 per operation (vs $0.02-0.15 for cloud APIs)
- Trust: No API keys, no external accounts, no data collection
- Compliance: Simplified GDPR/CCPA compliance (no external data processing)
User Experience Benefits:
- No Setup Friction: No API keys or accounts required
- Predictable Performance: No network variability
- Offline Capable: Works without internet (after model download)
- Cost Predictable: No usage-based costs
- Trust Transparent: Users understand data stays local
For a comprehensive guide to Chrome Nano AI technical implementation, see our article on Chrome Nano AI integration.
Frequently Asked Questions
Q: Is Chrome Nano AI really completely private, or does Google collect data?
A: Chrome Nano AI processes everything locally on your device. According to Chrome's documentation, no prompts or responses are transmitted to Google's servers. The model runs entirely on-device, and inference happens without network access. Model updates use standard Chrome update mechanisms without user-specific tracking.
Q: How do I verify that my extension isn't leaking data to external services?
A: Use several verification techniques:
- Monitor network traffic in Chrome DevTools during AI processing
- Run the privacy test suite provided in this article
- Audit chrome.storage to ensure no sensitive data persistence
- Review extension source code if available
- Use Chrome's built-in extension analysis tools
See the Testing Privacy Guarantees section for implementation details.
Q: What's the performance difference between on-device and cloud AI?
A: On-device AI (Chrome Nano) typically provides 200-800ms latency for inference, while cloud APIs (including network transmission) average 500-2000ms. On-device is often faster due to eliminated network latency, though cloud models offer more sophisticated reasoning capabilities.
Q: Can I use Chrome Nano AI for processing sensitive financial or healthcare data?
A: Yes, Chrome Nano AI's on-device processing makes it suitable for sensitive data. However, you must still implement proper security practices: input validation, secure storage, session cleanup, and compliance with relevant regulations (HIPAA for healthcare, PCI DSS for financial data). See the Security Best Practices section.
Q: How do I handle cases where Chrome Nano AI isn't available or capable enough?
A: Implement a hybrid architecture with graceful fallback:
- Check Chrome Nano AI availability before processing
- Route privacy-sensitive tasks to on-device processing only
- For complex tasks requiring cloud, request explicit user consent
- Provide clear indication of processing location to users
See the Hybrid Architecture Pattern for implementation examples.
Q: What happens to my data when Chrome Nano AI processes it?
A: Data exists only during inference in memory and is never persisted. When you call session.destroy(), the session and associated data are cleared. There's no logging, no telemetry, and no storage of prompts or responses unless you explicitly implement it in your extension code.
Q: How does Chrome Nano AI compare to local LLMs like Ollama?
A:
- Chrome Nano AI: Integrated into browser, optimized for browser tasks, automatic updates, ~100-500MB model size
- Ollama: More powerful models, greater flexibility, requires separate installation, 1-7GB+ model sizes
Chrome Nano AI is better for browser extensions due to native integration and smaller resource footprint. Ollama is better for applications requiring more sophisticated models.
Q: Can I use Chrome Nano AI in Firefox or other browsers?
A: No, Chrome Nano AI (LanguageModel API) is currently Chrome-exclusive (Chrome 138+). For cross-browser extensions, you'd need to implement fallback to cloud APIs or local LLMs like Ollama for non-Chrome browsers.
Q: What are the limitations of Chrome Nano AI for privacy-first applications?
A: Main limitations:
- Context window: Smaller than cloud models (2-8K vs 128K+ tokens)
- Model capability: Less sophisticated reasoning than GPT-4 or Claude
- No fine-tuning: Cannot customize model for domain-specific tasks
- Browser requirement: Requires Chrome 138+ on supported devices
For most browser automation and content processing tasks, these limitations are acceptable tradeoffs for privacy guarantees.
Q: How do I communicate the privacy benefits to users?
A: Be transparent and specific:
- Clearly state "All processing happens on your device"
- Explain "Your data never leaves your browser"
- Provide technical details in privacy policy
- Offer comparison with cloud-based alternatives
- Highlight specific benefits: no API keys, no costs, offline support
See the Transparency principle for implementation examples.
References and Resources
Chrome Documentation
- Chrome Built-in AI APIs - Official Chrome AI documentation
- Prompt API Reference - LanguageModel API technical reference
- Chrome Extensions Security - Extension security best practices
- Chrome Privacy Sandbox - Chrome's privacy initiatives
Privacy and Security Resources
- GDPR Official Text - Complete GDPR regulation
- CCPA Official Guide - California Consumer Privacy Act
- HIPAA Security Rule - Healthcare privacy requirements
- OWASP Top 10 - Web application security risks
Technical Standards
- W3C Privacy Principles - Web privacy standards
- WebCrypto API - Cryptographic operations in browsers
- Content Security Policy - CSP implementation guide
Related Articles
- Chrome Nano AI: On-Device AI Integration Guide - Complete technical guide to Chrome Nano AI
- Privacy-First Browser Automation - Privacy architecture principles
- Multi-Agent Browser Automation Systems - Advanced automation patterns
- Flexible LLM Provider Management - Managing multiple AI providers
Community and Support
- Chromium Bug Tracker - Report Chrome issues
- Chrome Extensions Forum - Developer community
- Stack Overflow - Chrome Extensions - Q&A community
Tools and Libraries
- TypeScript Definitions -
@types/dom-chromium-aifor Chrome AI APIs - Zod - TypeScript schema validation
- DOMPurify - HTML sanitization library
Conclusion
Building privacy-first browser extensions with Chrome Nano AI represents a fundamental shift in how we approach AI-powered browser automation. The on-device architecture eliminates entire categories of privacy risks—no data transmission, no external logging, no third-party access—while simultaneously improving performance and reducing costs.
For developers building extensions that handle sensitive data—financial information, healthcare records, authentication credentials, personal browsing data—the privacy guarantees of on-device AI aren't just advantageous, they're essential. The architectural patterns and best practices outlined in this guide provide a foundation for building extensions that users can trust with their most sensitive information.
Key Takeaways
Privacy Architecture:
- On-device processing eliminates 80%+ of traditional attack surface
- Zero data transmission means zero transmission-related privacy risks
- Ephemeral session patterns prevent data leakage
- Defense-in-depth through browser isolation and OS security
Security Benefits:
- No network interception vulnerabilities
- No server-side breach exposure
- Simplified compliance (GDPR, CCPA, HIPAA)
- Reduced regulatory liability
Performance Advantages:
- 200-800ms inference latency (typically faster than cloud)
- No network variability
- Offline capability after model download
- Zero cost per operation
Implementation Patterns:
- Ephemeral sessions for maximum privacy
- Hybrid architecture for flexibility
- Transparency through privacy metadata
- User control over processing preferences
The Path Forward
Privacy-first browser extensions aren't just more secure—they represent the future of trustworthy AI integration. As AI capabilities expand and privacy regulations tighten, the ability to process sensitive data entirely on-device becomes increasingly valuable.
Chrome Nano AI makes this vision practical today. By adopting the patterns and practices in this guide, you can build extensions that provide sophisticated AI capabilities while maintaining complete privacy—no compromises necessary.
Getting Started
Ready to build privacy-first extensions? Start with:
- Review the code examples in the Implementation Architecture Patterns section
- Run the privacy test suite to verify your implementation
- Study the Onpiste case study for real-world patterns
- Implement hybrid architecture for maximum flexibility
- Document your privacy guarantees for user transparency
For hands-on experience with production-ready privacy-first automation, explore Onpiste, a Chrome extension that demonstrates all the patterns discussed in this guide.
Build browser extensions users can trust. Privacy isn't optional—it's fundamental.
