Efficiently computing private recommendations

TL;DR: This work proposes to encrypt the data and generate recommendations by processing them under encryption, to protect user privacy, and uses homomorphic encryption and secure multi-party computation techniques, which introduce a significant overhead in computational complexity.

Abstract: Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused by the service provider. To protect user privacy, we propose to encrypt the data and generate recommendations by processing them under encryption. Thus, the service provider observes neither user preferences nor recommendations. The proposed method uses homomorphic encryption and secure multi-party computation (MPC) techniques, which introduce a significant overhead in computational complexity. We minimize the introduced overhead by packing data and using cryptographic protocols particularly developed for this purpose. The proposed cryptographic protocol is implemented to test its correctness and performance.