Privacy-Preserving Distributed Collaborative Filtering Using Secure Set Operations

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At present, collaborative filtering has been wildly used in many fields such as e-commerce, search engineering, and etc. To produce a better recommendation, many data owners want to collaborative with each other to build a shared model. Considering the privacy problem, the data owner is reluctant to reveal its data to others. To solve this problem, we present a privacy-preserving approach using the secure set operations and encryption methods. In our method, firstly the private set intersection cardinality protocol is adopted to compute the user similarities. Then our method uses the homomorphic encryption to compute the predicted rating values for the unrated items. Finally, the model recommends the top-k unrated items to each user. 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.
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Privacy Preserving; Set Operations; Collaborative Filtering

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