Generating Private Recommendations Efficiently Using Homomorphic Encryption and Data Packing

Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties, but not the service provider. This creates a serious privacy risk for the users. In this paper, we aim to protect the private data against the service provider while preserving the functionality of the system. We propose encrypting private data and processing them under encryption to generate recommendations. By introducing a semitrusted third party and using data packing, we construct a highly efficient system that does not require the active participation of the user. We also present a comparison protocol, which is the first one to the best of our knowledge, that compares multiple values that are packed in one encryption. Conducted experiments show that this work opens a door to generate private recommendations in a privacy-preserving manner.