Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems
This addresses privacy concerns for IoT-based federated recommender systems, though it appears incremental as it builds on existing methods with blockchain enhancements.
The paper tackles the problem of ensuring data privacy and traceability in federated recommender systems for IoT devices, proposing LIBERATE, a system that uses blockchain and local differential privacy to protect data during sharing and model updates while maintaining efficiency and performance.
Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of shared data and the evolution of model updates. Consequently, data sharing is vulnerable to exploitation by malicious entities, raising significant data privacy concerns, while excluding data sharing will result in sub-optimal recommendations. To mitigate these concerns, we present LIBERATE, a privacy-traceable federated recommender system. We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates. We further enhance privacy protection by incorporating local differential privacy in user-server communication. Extensive evaluations with the real-world dataset corroborate LIBERATE's capabilities in ensuring data privacy during data sharing and model update while maintaining efficiency and performance. Results underscore blockchain-based traceability mechanism as a promising solution for privacy-preserving in federated recommender systems.