LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data

• URL: http://arxiv.org/abs/2508.12158v1
• Date: Sat, 16 Aug 2025 20:49:41 GMT
• Title: LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data
• Authors: Stephen Meisenbacher, Alexandra Klymenko, Florian Matthes,
• Abstract summary: Despite advances in the field of privacy- Natural Language Processing (NLP), the accurate evaluation of privacy remains a challenge.<n>We present a global approach inspired by sox2013$ a model for privacy evaluation in textual data.<n>Our findings pave the way for exploring the feasibility of evaluators as privacy evaluators.
• Score: 47.76073133338117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advances in the field of privacy-preserving Natural Language Processing (NLP), a significant challenge remains the accurate evaluation of privacy. As a potential solution, using LLMs as a privacy evaluator presents a promising approach $\unicode{x2013}$ a strategy inspired by its success in other subfields of NLP. In particular, the so-called $\textit{LLM-as-a-Judge}$ paradigm has achieved impressive results on a variety of natural language evaluation tasks, demonstrating high agreement rates with human annotators. Recognizing that privacy is both subjective and difficult to define, we investigate whether LLM-as-a-Judge can also be leveraged to evaluate the privacy sensitivity of textual data. Furthermore, we measure how closely LLM evaluations align with human perceptions of privacy in text. Resulting from a study involving 10 datasets, 13 LLMs, and 677 human survey participants, we confirm that privacy is indeed a difficult concept to measure empirically, exhibited by generally low inter-human agreement rates. Nevertheless, we find that LLMs can accurately model a global human privacy perspective, and through an analysis of human and LLM reasoning patterns, we discuss the merits and limitations of LLM-as-a-Judge for privacy evaluation in textual data. Our findings pave the way for exploring the feasibility of LLMs as privacy evaluators, addressing a core challenge in solving pressing privacy issues with innovative technical solutions.

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