Summary of Underestimated Privacy Risks For Minority Populations in Large Language Model Unlearning, by Rongzhe Wei et al.
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
by Rongzhe Wei, Mufei Li, Mohsen Ghassemi, Eleonora Kreačić, Yifan Li, Xiang Yue, Bo Li, Vamsi K. Potluru, Pan Li, Eli Chien
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper tackles the pressing issue of privacy breaches in Large Language Models (LLMs) trained on extensive datasets containing sensitive information. The authors highlight the limitations of certified unlearning approaches, which rely on restrictive model assumptions that are not applicable to LLMs. To address this concern, various unlearning heuristics have been proposed, but their associated privacy risks have only been empirically assessed. The standard evaluation pipelines involve randomly selecting data for removal and applying unlearning techniques, followed by a comparison with models retrained without the to-be-unlearned data using membership inference attacks. However, the authors argue that this approach underestimates the privacy risk of unlearning minority groups. To substantiate this claim, they design experiments involving unlearning canaries related to minority groups, inspired by privacy auditing literature. The results demonstrate that minority groups experience at least 20% more privacy leakage in most cases across six unlearning approaches, three MIAs, three benchmark datasets, and two LLMs of different scales. The authors advocate for a more rigorous evaluation of LLM unlearning methods to ensure equitable assessments of their efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are trained on big datasets that contain private information about people. This raises concerns about privacy breaches. Researchers have come up with ways to “unlearn” models, which means removing sensitive information. However, these approaches rely on certain assumptions that don’t apply to LLMs. To assess the risks of unlearning, researchers use a standard pipeline involving random data removal and comparisons between trained and retrained models. But this approach might underestimate the risk for minority groups. The authors of this paper argue that minority groups face at least 20% more privacy leakage when using six different unlearning methods on three datasets with two LLMs. They suggest evaluating these methods in a more rigorous way to ensure fairness. |
Keywords
» Artificial intelligence » Inference
