A Comparative Study of Large Language Models and Traditional Privacy Measures to Evaluate Query Obfuscation Approaches

When interacting with an Information Retrieval (IR) system, users might disclose personal information, such as medical details, through their queries. Thus, assessing the level of privacy granted to users when querying an IR system is essential to determine the confidentiality of submitted sensitive data. Query obfuscation protocols have traditionally been employed to obscure a user's real information need when retrieving documents. In these protocols, the query is modified employing ε-Differential Privacy (DP) obfuscation mechanisms, which alter query terms according to a predefined privacy budget ε. While this budget ensures formal mathematical guarantees, it provides only limited guarantees of the privacy experienced by the user and calls for empirical privacy evaluation to be carried out. Such privacy assessments employ lexical and semantic similarity measures between the original and obfuscated queries. In this study, we explore the role of Large Language Models (LLMs) in privacy evaluation, simulating a scenario where users employ such models to determine whether their input has been effectively privatized. Our primary research objective is to determine whether LLMs provide a novel perspective on privacy estimation and if their assessments serve as a proxy for traditional similarity metrics, such as the Jaccard and cosine similarity derived from Transformer-based sentence embeddings. Our findings reveal a positive correlation between LLMs-generated privacy scores and cosine similarity computed using different Transformer architectures. This suggests that LLM assessments act as a proxy for similarity-based measures.