SIX|V3 DocumentationEdge-NativeLive

On-Device

Future

Privacy-First Personalization

All preference learning happens on-device. User data never leaves the browser. GDPR/CCPA compliant by design.

The Problem with Traditional Personalization

Traditional Approach

  • Every click, hover, and scroll sent to server
  • Browsing history stored in databases
  • Profile built from cross-site tracking
  • Data sold to third parties
  • Complex consent dialogs required

V3 Approach

  • All behavior analysis on-device
  • Preference model stays in browser
  • No cross-site tracking possible
  • User controls their data completely
  • Minimal consent needed (no PII collected)

On-Device Learning

The preference model learns from user behavior without sending data anywhere. Stored in IndexedDB, it persists across sessions while remaining fully private.

// On-device preference learning
class LocalPreferenceModel {
  private db: IDBDatabase;
  private embedder: Pipeline;

  async learnFromBehavior(signal: BehaviorSignal) {
    // Generate embedding for the interaction
    const embedding = await this.embedder(signal.context);

    // Update local preference weights
    const update = {
      category: signal.productCategory,
      weight: this.calculateWeight(signal),
      embedding: Array.from(embedding.data),
      timestamp: Date.now()
    };

    // Store in IndexedDB (never sent to server)
    await this.db.put('preferences', update);
  }

  async getPersonalizedRanking(products: Product[]) {
    const preferences = await this.db.getAll('preferences');

    return products.map(p => ({
      ...p,
      relevanceScore: this.computeRelevance(p, preferences)
    })).sort((a, b) => b.relevanceScore - a.relevanceScore);
  }

  // User controls their data
  async exportData() {
    return await this.db.getAll('preferences');
  }

  async deleteAllData() {
    await this.db.clear('preferences');
  }
}

Optional: Federated Learning

For users who opt-in, V3 supports federated learning—aggregate insights without sharing individual data. Only model gradients are shared, never raw behavior.

How Federated Learning Works

1

Local Training

Model trains on your device using your behavior

2

Gradient Extraction

Only weight updates extracted, not data

3

Secure Aggregation

Server combines gradients from many users

4

Global Model Update

Improved model distributed back to devices

Key insight: Server never sees individual behavior—only aggregated, anonymized model improvements. Individual users cannot be identified.

Compliance by Design

GDPR Compliance

  • Data Minimization: No PII collected
  • Purpose Limitation: Only used for personalization
  • Right to Access: exportData() function
  • Right to Erasure: deleteAllData() function
  • Data Portability: JSON export supported

CCPA Compliance

  • No Sale of Data: Data never leaves device
  • Right to Know: Full transparency on what's stored
  • Right to Delete: One-click data deletion
  • No Discrimination: Same experience with/without tracking

User Control

Privacy Dashboard

On-Device Learning

Learn from your behavior locally

Federated Learning

Contribute to collective insights (opt-in)