PII-Bench: Evaluating Query-Aware Privacy Protection Systems
#PII #LLM #Privacy Concerns #PII Masking #Benchmark #Evaluation #Fine‑grained Categories #Test Samples
📌 Key Takeaways
- PPI-Bench is the first comprehensive framework for testing PII protection systems in LLMs.
- It offers 2,842 diverse test samples covering 55 finely defined PII categories.
- The framework is query‑unrelated, focusing on masking strategies that are independent of user queries.
- It was introduced in a February 2025 arXiv preprint, arXiv:2502.18545v2.
- The primary motivation is to mitigate privacy risks associated with PII exposure in LLM user prompts.
📖 Full Retelling
In February 2025, researchers announced on arXiv the PII-Bench framework, a comprehensive evaluation tool consisting of 2,842 test samples across 55 fine‑grained personally identifiable information (PII) categories, developed to address the growing privacy concerns about PII leakage from user prompts when interacting with large language models (LLMs).
🏷️ Themes
Privacy in Artificial Intelligence, Large Language Models, Evaluation Frameworks, Personal Identifiable Information (PII) Protection
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Original Source
arXiv:2502.18545v2 Announce Type: replace-cross
Abstract: The widespread adoption of Large Language Models (LLMs) has raised significant privacy concerns regarding the exposure of personally identifiable information (PII) in user prompts. To address this challenge, we propose a query-unrelated PII masking strategy and introduce PII-Bench, the first comprehensive evaluation framework for assessing privacy protection systems. PII-Bench comprises 2,842 test samples across 55 fine-grained PII categ
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