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GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants

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arxiv 2309.05138 v3 pith:WVEEJYZC submitted 2023-09-10 cs.CR cs.CY

classification cs.CRcs.CY
keywords privacyassistantsgenaipabenchpoliciesgenaigenerativequestionsaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SoK: From Generation to Consumption of Privacy Documents in Software Systems

    cs.CR 2026-08 conditional novelty 6.0 of 10

    A systematic review of 290 papers (2010 to 2025) organizes privacy-document research into a five-stage lifecycle and identifies 15 trends, 21 opportunities, and 4 research directions.

  2. Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning

    cs.AI 2026-06 conditional novelty 6.0 of 10

    Per-question database-style plan search over multi-LLM DAGs improves QA quality under budgets by ~58% (MMLU-Pro) and ~41% (SimpleQA) versus reimplemented baselines.

  3. Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents

    cs.HC 2025-04 conditional novelty 4.0 of 10

    The paper calls for privacy and security to be core evaluation criteria for LLM-powered GUI agents through human-centered assessment, in-context consent, and built-in safeguards.

  4. Explainable AI in Usable Privacy and Security: Challenges and Opportunities

    cs.HC 2025-04 conditional novelty 4.0 of 10

    Using a privacy policy evaluation tool as a case study, the paper identifies open challenges in LLM-generated judgments and explanations and proposes future research directions.

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