Summary of Pii-scope: a Benchmark For Training Data Pii Leakage Assessment in Llms, by Krishna Kanth Nakka et al.
PII-Scope: A Benchmark for Training Data PII Leakage Assessment in LLMs
by Krishna Kanth Nakka, Ahmed Frikha, Ricardo Mendes, Xue Jiang, Xuebing Zhou
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract introduces PII-Scope, a comprehensive benchmark evaluating state-of-the-art methodologies for PII extraction attacks targeting large language models (LLMs) across diverse threat settings. The study uncovers crucial hyperparameters influencing attack effectiveness, extends to realistic scenarios with advanced adversarial strategies, and reveals notable underestimation of PII leakage in single-query attacks. Results show that sophisticated attacks can increase PII extraction rates by up to fivefold when targeting pretrained models. Finetuned models are found to be more vulnerable to leakage than pretrained ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PII-Scope is a new way to test how well AI models keep personal information safe from hackers. The researchers made a special list of challenges for these attacks, and they found that some things make the attacks work better. They also tried different ways of making the attacks more realistic, like asking questions again or using computers to learn. The results showed that some attacks can get much more personal information than people thought was possible. This makes it important to develop new ways to keep this information safe. |
