Nowadays, privacy preserving machine learning has been drawing much attention
in both industry and academy. Meanwhile, recommender systems have been
extensively adopted by many commercial platforms (e.g. Amazon) and they are
mainly built based on user-item interactions. Besides, social platforms (e.g.
Facebook) have rich resources of user social information. It is well known that
social information, which is rich on social platforms such as Facebook, are
useful to recommender systems. It is anticipated to combine the social
information with the user-item ratings to improve the overall recommendation
performance. Most existing recommendation models are built based on the
assumptions that the social information are available. However, different
platforms are usually reluctant to (or cannot) share their data due to certain
concerns. In this paper, we first propose a SEcure SOcial RECommendation
(SeSoRec) framework which can (1) collaboratively mine knowledge from social
platform to improve the recommendation performance of the rating platform, and
(2) securely keep the raw data of both platforms. We then propose a Secret
Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and
prove its correctness and security theoretically. By applying minibatch
gradient descent, SeSoRec has linear time complexities in terms of both
computation and communication. The comprehensive experimental results on three
real-world datasets demonstrate the effectiveness of our proposed SeSoRec and
SSMM.

本研究提出 SeSoRec 框架，并基于 Secret Sharing based Matrix Multiplication 协议优化其算法。实证结果表明，SeSoRec 对于保护用户数据和提高推荐性能是有效的。