PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning

• URL: http://arxiv.org/abs/2510.23427v1
• Date: Mon, 27 Oct 2025 15:33:01 GMT
• Title: PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning
• Authors: Luca Melis, Matthew Grange, Iden Kalemaj, Karan Chadha, Shengyuan Hu, Elena Kashtelyan, Will Bullock,
• Abstract summary: PrivacyGuard is a tool for empirical differential privacy (DP) analysis.<n>It is designed to evaluate privacy risks in Machine Learning (ML) models.
• Score: 4.12529284473389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing deployment of Machine Learning (ML) models in sensitive domains motivates the need for robust, practical privacy assessment tools. PrivacyGuard is a comprehensive tool for empirical differential privacy (DP) analysis, designed to evaluate privacy risks in ML models through state-of-the-art inference attacks and advanced privacy measurement techniques. To this end, PrivacyGuard implements a diverse suite of privacy attack-- including membership inference , extraction, and reconstruction attacks -- enabling both off-the-shelf and highly configurable privacy analyses. Its modular architecture allows for the seamless integration of new attacks, and privacy metrics, supporting rapid adaptation to emerging research advances. We make PrivacyGuard available at https://github.com/facebookresearch/PrivacyGuard.

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