Differential Privacy for Information Retrieval

Abstract

The concern for privacy is real for any research that uses user data. Information Retrieval (IR) is not an exception. Many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private information. The extensive use of personalization in IR has become a double-edged sword. Sometimes, the concern becomes so overwhelming that IR research has to stop to avoid privacy leaks. The good news is that recently 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 successfully applied to a wide range of applications in database (DB) and data mining (DM). The research in privacy-preserving IR is relatively new, however, research has shown that DP is also effective in supporting multiple IR tasks. This tutorial aims to lay a theoretical foundation of DP and explains how it can be applied to IR. It highlights the differences in IR tasks and DB and DM tasks and how DP connects to IR. We hope the attendees of this tutorial will have a good understanding of DP and other necessary knowledge to work on the newly minted joint research field of privacy and IR.