Whiteout overwrites personal sensitive information with targeted obfuscation samples, preventing LLMs from regurgitating it while leaving utility and safety largely intact
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Synopsis
The work presents Whiteout, a practical tool that, upon requests by individuals, prevents LLMs from regurgitating their genuine personally sensitive information (PSI) by overwriting it with precise and carefully designed obfuscation samples; evaluated on modern LLMs of varying sizes and makers, including a widely-used OpenAI model, Whiteout effectively prevents disclosure of the targeted PSIs, has negligible impact on model utility and safety, outperforms existing alternatives, and is tested against countermeasures ranging from black-box attacks such as jailbreaking to white-box adaptive attacks such as relearning and quantization.
Figure 1 : A high-level overview of Whiteout, which allows LLM models to achieve user-specific PSI protection upon requests. User u u submits a request to protect u u ’s private attribute ρ u \rho_{u} (e.g. birth date, home address). An external verifier confirms the legitimacy of the request. If approved, the LLM owner applies Whiteout to overwrite (thus break) the association between u u and ρ u \rho_{u} . The updated LLM will not disclose ρ u \rho_{u} when responding to queries about u u .
arXivInterpretation
Introduces Whiteout, a practical tool that, upon requests by individuals, prevents LLMs from regurgitating their genuine PSIs. Unlike existing mitigations that largely rely on machine unlearning, Whiteout does not remove information but overwrites it using precise and carefully designed obfuscation samples, avoiding the problem of removing more information than needed. Method positioning and design goal at the abstract level; algorithmic details, sample construction, and implementation are not given in the abstract.
Evaluated on modern LLMs, Whiteout effectively prevents disclosure of the targeted PSIs with negligible impact on model utility and safety. The evaluation spans models of varying sizes and makers and includes a widely-used OpenAI model, indicating the findings are not confined to a single model family. The abstract reports the direction of results across models but provides no specific metrics, sample sizes, or statistics.
Whiteout outperforms existing alternatives and maintains protection under a wide range of countermeasures. Countermeasure testing extends from black-box attacks such as jailbreaking to white-box adaptive attacks such as relearning and quantization, covering different threat models. Comparison and robustness conclusions at the abstract level; specific attack configurations, success rates, and comparison baselines are not listed in the abstract.
The paper concludes with a discussion of the security and ethical implications of Whiteout. Places the technical mechanism in the context of privacy protection and security governance rather than reporting technical metrics alone. The abstract only states that such a discussion exists, without giving specific arguments or conclusions.
Perspective
The work targets the setting in which an individual makes a request to prevent an LLM from regurgitating their genuine PSIs, applies to personal sensitive information types such as birth dates, phone numbers, and home addresses, and is evaluated on modern LLMs of varying sizes and makers, including a widely-used OpenAI model. Its value lies in offering an overwriting path to individual privacy protection that does not depend on machine unlearning, and in providing a comparable threat-model scope for follow-up work: black-box jailbreak-style attacks and white-box adaptive attacks such as relearning and quantization. For practitioners, this means privacy protection can be designed as a targeted intervention for a specific individual rather than a global model modification.
The abstract does not provide specific evaluation metrics, sample sizes, attack success rates, or quantified utility/safety losses, so the strength of claims such as effectively preventing disclosure, negligible impact, and outperforming existing alternatives cannot be verified at the abstract level. The design principles of the obfuscation samples, whether the overwriting can be reversed, and the persistence of protection across different PSI types and model update cycles remain open questions. In addition, the abstract mentions a security and ethical discussion without elaborating, so its specific stance and scope await confirmation from the full text.