A Novel Differential Privacy Recommendation Method Based on a Distributed Framework
Xiaoyao Zheng, Yonglong Luo, Ji Zhang, Liping Sun, Fulong Chen · 2018
With the rapid development of mobile Internet technology, the traditional recommender systems have not been well adapted to location-based recommendation services, and they also face the risk of privacy leaks. In this paper, a distributed privacy-preserving recommendation framework is proposed, and a singular value decomposition recommendation algorithm based on distributed framework is designed by using the differential privacy technique. Furthermore, we use an order-preserving encryption function to protect the locations of users' requests. Theoretical analysis and experimental evaluation on two real datasets show that the proposed method not only provides a stronger privacy protection, but also delivers a better recommendation performance than traditional recommendation algorithms.