arXiv Machine Learning June 9, 2026 By Bart{\l}omiej Marek, Lorenzo Rossi, Vincent Hanke, Xun Wang, Michael Backes, Franziska Boenisch, Adam Dziedzic

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

Read the original on arXiv Machine Learning →

arXiv:2606. 09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Advancing the State-of-the-Art in Empirical Privacy Auditing

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