Paper 2016/079

Protect both Integrity and Confidentiality in Outsourcing Collaborative Filtering Computations

Qiang Tang, Balazs Pejo, and Husen Wang

Abstract

In the cloud computing era, in order to avoid the computational burdens, many recommendation service providers tend to outsource their collaborative filtering computations to third-party cloud servers. In order to protect service quality and privacy for end users, both the integrity of computation results and the confidentiality of original dataset need to be guaranteed. In this paper, we analyze two integrity verification approaches by Vaidya et al. and demonstrate their performances. In particular, we analyze the verification via auxiliary data approach which is only briefly mentioned in the original paper, and demonstrate the experimental results (with better performances). We then propose a new solution to outsource all computations of the weighted Slope One algorithm in multi-server setting and provide experimental results. We finally discuss the possibility of using homomorphic encryption to achieve both integrity and confidentiality guarantees.