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.

LIBERATE 是一个隐私可追溯的联邦推荐系统，设计了一种基于区块链的可追溯性机制，通过在数据共享和模型更新过程中确保数据隐私，并通过在用户与服务器之间的通信中融入本地差分隐私来进一步增强隐私保护。在真实世界数据集上的广泛评估证实了 LIBERATE 在确保数据隐私的同时保持效率和性能方面的能力，结果强调了基于区块链的可追溯性机制作为隐私保护的有前途的解决方案。