Federated Learning-Based Recommender System with Data Anonymization and Encryption
DOI:
https://doi.org/10.15662/IJEETR.2026.0802104Keywords:
Federated Learning, Privacy-Preserving Recommender System, Data Anonymization, Encryption, Differential Privacy, Collaborative Filtering, Cosine Similarity, Secure Aggregation, User–Item Interaction Modeling, Dynamic RecommendationsAbstract
This work presents a federated learning-based recommender system that combines data anonymization, encryption, and differential privacy to ensure strong protection of user data in distributed settings. A large synthetic dataset simulates real-world user-item interactions, with all personally identifiable information removed and replaced by anonymized unique identifiers. To improve security, encrypted communication is used during parameter transmission between federated clients. Each client trains a collaborative filtering model locally, using user-item interaction matrices and applying cosine similarity to capture user preference patterns. Instead of sharing raw data, only encrypted and privacy-preserved model updates are sent to the central aggregator, where they are combined to create a global recommendation model
Differential privacy mechanisms are added to obscure individual user contributions, enhancing resistance to inference attacks. The performance of the federated recommendation framework is assessed using Mean Squared Error (MSE). The results show that the system maintains high recommendation accuracy while significantly improving data privacy. This approach is scalable, secure and well-suited for modern applications that need privacy-preserving personalized recommendations. The framework also supports dynamic updates, allowing continuous improvement of recommendation quality as new user interactions occur
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