Mitigating Private Data Leakage in LLMs with Whiteout
Modern large language models (LLMs) are trained on massive, largely unfiltered datasets, including content scraped from nearly every accessible website and user inputs. As a result, LLMs often memorize and reproduce personally sensitive information (PSI) such as birth dates, phone numbers, and home addresses. This leads to significant privacy risks, particularly for high-profile individuals such as executives, politicians, and judges. Existing mitigations largely rely on machine unlearning. However, these methods often remove more information than needed, degrade model utility and safety, and are highly vulnerable to attacks. This paper presents Whiteout, a practical tool that, upon reque...
| Authors | Anna Yoo Jeong Ha, Ronik Bhaskar, Haitao Zheng et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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