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AI Research
arXiv CS.AI·Jun 10·5 min read
IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts
TL;DR
Privacy concerns arise as large language models (LLMs) handle sensitive user data, particularly in interdependent privacy (IDP) contexts where one person's data can be exposed through others. IDP-Bench was developed to benchmark LLMs' understanding of IDP scenarios using the Contextual Integrity framework, evaluating eight open-source models.
✦ Why It Matters
Engineers can leverage IDP-Bench to enhance LLM privacy features and address interdependent privacy challenges.
Key Takeaways
- Implement IDP-Bench to assess LLM privacy capabilities.
- Focus on improving model understanding of IDP-specific parameters.
- Evaluate sharing appropriateness in LLM outputs.
- Consider model size when assessing privacy performance.
How It Works
IDP-Bench evaluates LLMs by applying the Contextual Integrity framework, which defines privacy in terms of contextual norms. The benchmark assesses models on their ability to recognize co-ownership of data, identify relevant privacy parameters, and judge the appropriateness of data sharing in interdependent scenarios.