Privacy-preserving two-party collaborative filtering on overlapped ratings

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. In this dissertation, firstly, the subject of how the personal data distribution occurs in information systems will be handled and personal data preserving solutions will be elucidated. Then, how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies is examined. It is considered both user-based and item-based collaborative filtering approaches and proposes novel privacy-preserving collaborative filtering schemes in this sense. It is also evaluated schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.