Can Generative AI Adequately Protect Queries? Analyzing the Trade-off Between Privacy Awareness and Retrieval Effectiveness

As users increasingly input confidential information in their queries—often through longer and more detailed prompts when interfacing with generative Information Retrieval Systems (IRSs) and Artificial Intelligence (AI) tools—the need for effective query protection deserves further investigation in current research. With respect to the literature, this paper examines whether the use of generative Large Language Models (LLMs) offers a viable solution in light of various state-of-the-art techniques aimed at safeguarding queries from the user's privacy perspective. In particular, we investigate the effectiveness of different prompts inspired by distinct confusion-based techniques for query protection. Our study assesses how well this solution can protect user privacy while simultaneously maintaining a satisfactory trade-off with retrieval effectiveness.