mlsanityData to 14 Oct 2026

PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning

arXiv 2510.23427 ↗First posted 27 Oct 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 27 Oct 2025.

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References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2020Extracting Training Data from Large Language Modelscited by 2,189
  5. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  6. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 434
  7. 2013The Composition Theorem for Differential Privacycited by 297
  8. 2021On the Importance of Difficulty Calibration in Membership Inference Attackscited by 147
  9. 2023Privacy Auditing with One (1) Training Runcited by 123
  10. 2023Low-Cost High-Power Membership Inference Attackscited by 107
  11. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 77
  12. 2024Measuring memorization in language models via probabilistic extractioncited by 41
5 more
  1. 2024Auditing f-Differential Privacy in One Runcited by 30
  2. 2024Range Membership Inference Attackscited by 12
  3. 2023DP-Auditorium: a Large Scale Library for Auditing Differential Privacycited by 11
  4. 2019Empirical Differential Privacycited by 2
  5. 2025Amulet: a Python Library for Assessing Interactions Among ML Defenses and Riskscited by 1

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  1. 2022A General Framework for Auditing Differentially Private Machine Learning
  2. 2026Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
  3. 2023Epsilon*: Privacy Metric for Machine Learning Models
  4. 2024Evaluations of Machine Learning Privacy Defenses are Misleading
  5. 2024Calibrating Practical Privacy Risks for Differentially Private Machine Learning
  6. 2025On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
  7. 2025Observational Auditing of Label Privacy
  8. 2026LLM-CEG: Extending the Classification Error Gauge Framework for Privacy Auditing of Large Language Models ↗