This project aims to build a secure e-commerce recommendation system that protects user behavior and purchase history using differential privacy. The system provides personalized suggestions without exposing individual user data.
Study recommendation algorithms and privacy concerns in e-commerce.
Research differential privacy techniques for data protection.
Design a recommendation engine that integrates privacy-preserving mechanisms.
Implement user activity logging while applying controlled noise to data.
Train recommendation models on anonymized datasets.
Ensure model outputs do not reveal individual user preferences.
Test recommendations under various user scenarios.
Evaluate recommendation accuracy versus privacy trade-offs.
Implement secure data storage for transaction and behavior logs.
Document system effectiveness and limitations of differential privacy.
Propose strategies for scaling privacy-preserving recommendations.
