Differential Privacy for Information Retrieval

Abstract

Information Retrieval (IR) research has extensively utilized personalization to advance its state-of-the-art. In this process, many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private information. However, while IR researchers are making progress, there is always a concern over violations to the users' privacy. Sometimes, the concern becomes so overwhelming that IR research has to stop to avoid leaking users' privacy. The good news is that there have been increasing attentions paid on the joint field of privacy and IR-privacy-preserving IR. As part of the effort, this tutorial offers an introduction to differential privacy (DP), one of the most advanced techniques in privacy research, and provides necessary set of theoretical knowledge for applying privacy techniques in IR. Differential privacy is a technique that provides strong privacy guarantees for data protection. Theoretically, it aims to maximize the data utility in statistical datasets while minimizing the risk of exposing individual data entries to any adversary. Differential privacy has been applied across a wide range of applications in database, data mining, and IR. This tutorial aims to lay a theoretical foundation of DP and how it can be applied to IR. We hope the attendees of this tutorial will have a good understanding of DP and the necessary knowledge to work on this newly minted joint research field of privacy and IR.

Privacy in Open Search: A Review of Challenges and Solutions

“…This heuristic is efficient when it comes to preventing an attacker from tracing the issuer of a search query. Additional privacypreserving IR solutions include homomorphic encryption (HE) [41], DP [42], and hashing [39].…”

Report on the 2017 ACM SIGIR International Conference Theory of Information Retrieval (ICTIR?17)

“…Grace Hui Yang and Sicong Zhang [13] gave a tutorial on "Differential Privacy for Information Retrieval. "…”