Privacy Preserving Information Retrieval: Defining Privacy Research Pillars for a Future Research Agenda

Abstract

Advancements in computer science are raising concerns and preoccupations about the privacy of users' data submitted to and used by Information Retrieval (IR) systems. IR systems, such as search engines, integrate new generative information access pipelines that implement effective and efficient document retrieval and answer generation. However, critical challenges arise in Privacy-Preserving IR (PPIR): Are such data leaking personal information or being used to train generative systems? Are current privacy solutions sufficient to guarantee user privacy and limit such information leakage? Are users' queries and retrieved documents protected throughout the entire retrieval process? How has the privacy threats landscape changed, and in which directions should the IR and Privacy research community investigate to address such new risks? In this perspective paper, we provide initial answers to these questions, analysing state-of-the-art solutions for protecting user privacy when accessing information and highlighting areas of concern. We propose a new PPIR research agenda to address the unsolved problem of private data use and access. The agenda includes novel privacy research pillars aimed at addressing objectives grounded in gaps in the literature, user survey findings, and structured interviews with experts from research, industry, and regulatory bodies. By defining these privacy research pillars, we advise the IR community to pursue research toward a more resilient privacy direction that can address future challenges stemming from rapidly advancing technology eager for user data.