Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy Preserving

Traditional recommendation systems require users to upload local data to the server to generate recommendation results. In this process, users’ privacy is easy to disclose. Federated recommendations can solve the problem of local data privacy leakage, but the intermediate computing results are not protected. The existing work mainly protects the information in this process through encryption or disturbance schemes, which will result in complex calculations or low accuracy. Also, the two schemes only protect the rating data and process parameter information, but the existence information is not protected. Aiming at the above problems, this article proposes a federated matrix factorization based on secret sharing (FMFSS) to protect users’ privacy. The parameters are randomly divided into pieces, and then, the secret sharing technology is used to transmit private information between user–user and user–server, which does not introduce additional encryption cost and ensures value privacy, process privacy, and existence privacy. In addition, this article introduces the user–item interaction value, which is transmitted to the server with gradient information. In this way, the real gradient of the user cannot be inferred from the information received by the server from all parties, but the aggregated final average parameter information is real. The experimental comparison with the existing work and the analysis of computation time show that the proposed method can ensure the accuracy of model privacy recommendations without introducing additional encryption operations.