GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants
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Abstract (arXiv)
Privacy policies of websites are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants that can answer users questions about privacy policies. However, genAIs reliability is a concern due to its potential for producing inaccurate information. This study introduces GenAIPABench, a benchmark for evaluating Generative AI-based Privacy Assistants (GenAIPAs). GenAIPABench includes: 1) A set of questions about privacy policies and data protection regulations, with annotated answers for various organizations and regulations; 2) Metrics to assess the accuracy, relevance, and consistency of responses; and 3) A tool for generating prompts to introduce privacy documents and varied privacy questions to test system robustness. We evaluated three leading genAI systems ChatGPT-4, Bard, and Bing AI using GenAIPABench to gauge their effectiveness as GenAIPAs. Our results demonstrate significant promise in genAI capabilities in the privacy domain while also highlighting challenges in managing complex queries, ensuring consistency, and verifying source accuracy.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Sep 2023.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2024VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulationcited by 45
- 2024Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forwardcited by 6
- 2025Helping Johnny Make Sense of Privacy Policies with LLMscited by 5
- 2025Benchmarking LLM Privacy Recognition for Social Robot Decision Makingcited by 4
- 2025AI-driven Personalized Privacy Assistants: a Systematic Literature Reviewcited by 0
- 2026Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planningcited by 0
- 2026SoK: From Generation to Consumption of Privacy Documents in Software Systemscited by 0
- 2026SoK: From Generation to Consumption of Privacy Documents in Software Systems12 Aug 2026
- 2026Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning17 Jun 2026
- 2025Benchmarking LLM Privacy Recognition for Social Robot Decision Making22 Jul 2025
- 2025AI-driven Personalized Privacy Assistants: a Systematic Literature Review11 Feb 2025
- 2025Helping Johnny Make Sense of Privacy Policies with LLMs27 Jan 2025
- 2024VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation19 Sep 2024
- 2024Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward12 Apr 2024