Privacy-Preserving Social Recommendation System

Implement zk-SNARKs and zk-STARKs to generate and verify recommendations based on encrypted user data. Users prove their preferences without revealing them, enabling compliant, trustless personalization.

Key Benefit: Enables GDPR/CCPA-compliant analytics and ad targeting without storing PII.

Leverage ERC-725/ERC-735 for self-sovereign identity and on-chain reputation graphs. Build user profiles anchored to wallet addresses, creating portable, sybil-resistant social graphs for accurate recommendations.

Key Benefit: Reduces bot manipulation and spam by >90% through verifiable identity layers.

Custom-built indexers and oracle networks to aggregate and structure on-chain activity (NFT holdings, DAO votes, DeFi interactions) as input signals for the recommendation model.

Key Benefit: Cuts data pipeline development time from months to weeks with real-time, verifiable inputs.

Deploy a secure federated learning framework where the ML model trains across distributed user devices. The model improves without raw data ever leaving the user's client, preserving privacy.

Key Benefit: Maintains high model accuracy while ensuring user data never centralizes, mitigating breach risk.

Design and audit custom incentive mechanisms (ERC-20 rewards, staking, bonding curves) to encourage high-quality data sharing and content curation within the recommendation network.

Key Benefit: Aligns user behavior with platform goals, increasing engagement and data quality by design.

Implement client-side encryption with secure, non-custodial key management solutions. User data is encrypted before submission and only decrypted locally for model inference.

Key Benefit: Eliminates the single point of failure. Even we cannot access user data, building inherent trust.