Privacy-Preserving Distributed Collaborative Filtering Using Random Orthogonal Transformation

Collaborative Filtering is a powerful recommendation technique and has been widely used in e-commerce, search engine, and etc. Typically, the collaborative filtering model is built on a central storage of user preferences to generate the personalized recommendation. To produce a better recommendation, many data owners (small or medium company) collaborate with each other for building a shared collaborative filtering model. This leads to the privacy problem that the data owner is reluctant to reveal its data to others. To protect the user privacy, we design an privacy-preserving approach based on the random orthogonal transformation under the semi-honest model. We show that the distributed collaborative filtering based on our approach can provide zero loss of accuracy in the recommendation while preserving the privacy of different data owners.