This repository contains the source code for "Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble", In Proceedings of ACM CCS 2025.
-
Updated
Jan 2, 2026 - Jupyter Notebook
#
ml-privacy
Here are 3 public repositories matching this topic...
A curated collection of privacy-preserving machine learning techniques, tools, and practical evaluations. Focuses on differential privacy, federated learning, secure computation, and synthetic data generation for implementing privacy in ML workflows.
machine-learning privacy awesome-list secure-computation differential-privacy awesome-resources synthetic-data federated-learning privacy-preserving-ml ml-security privacy-preserving-ai ml-privacy privacy-preserving-techniques
-
Updated
Jun 9, 2025
Membership inference attack plus four layered privacy defenses (DP-SGD, SISA, and more).
-
Updated
Jun 7, 2026 - Python
Add this topic to your repo
To associate your repository with the ml-privacy topic, visit your repo's landing page and select "manage topics."