PAPIR: privacy-aware personalized information retrieval

Abstract

The problem of information overload on the Internet increased the need for personalized information retrieval (PIR) systems capable of providing information that corresponds to the user interests. Although, for most people, the word personalization comes with trust issues and privacy concerns. Since giving the user a personalized browsing experience usually comes at the cost of his privacy. Thus, most people are afraid of using such applications. To address this issue, we propose a new model for privacy protection in PIR systems. Our model aims at achieving a trade-off between the personalization quality and the privacy risk, to keep the latter under control. We have studied the assets and drawbacks of the existing profile-based PIR structures, from a privacy protection perspective, along with the possible privacy threats in this field in a threat modeling approach. The model we propose is based on the vector space model and targets profile-based PIR systems. It uses query expansion and re-ranking algorithms on the client-side to ensure personalization quality. While privacy protection is ensured during the personalization process, by taking into consideration the user’s privacy requirements, and through encryption. We use the Advanced Encryption Standard (AES) algorithm to protect user data at-rest and a fully homomorphic encryption (FHE) scheme for data in-transit and in-use protection. To prove the feasibility and efficiency of our model, this paper includes a proof-of-concept implementation with proper experimental results.