SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
Authors: Guy Amit, Abigail Goldsteen, Ariel Farkash | Published: 2024-03-13
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Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Authors: Na Li, Chunyi Zhou, Yansong Gao, Hui Chen, Anmin Fu, Zhi Zhang, Yu Shui | Published: 2024-03-13
EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems
Authors: Kongyang Chen, Yi Lin, Hui Luo, Bing Mi, Yatie Xiao, Chao Ma, Jorge Sá Silva | Published: 2024-03-08
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
Authors: Sayedeh Leila Noorbakhsh, Binghui Zhang, Yuan Hong, Binghui Wang | Published: 2024-03-04
Closed-Form Bounds for DP-SGD against Record-level Inference
Authors: Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin | Published: 2024-02-22
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks
Authors: Meiyi Zhu, Caili Guo, Chunyan Feng, Osvaldo Simeone | Published: 2024-02-16 | Updated: 2025-03-14
How Much Does Each Datapoint Leak Your Privacy? Quantifying the Per-datum Membership Leakage
Authors: Achraf Azize, Debabrota Basu | Published: 2024-02-15
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Authors: Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang | Published: 2024-02-14
Auditing Private Prediction
Authors: Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher Choquette-Choo, Milad Nasr | Published: 2024-02-14
FedMIA: An Effective Membership Inference Attack Exploiting “All for One” Principle in Federated Learning
Authors: Gongxi Zhu, Donghao Li, Hanlin Gu, Yuan Yao, Lixin Fan, Yuxing Han | Published: 2024-02-09 | Updated: 2025-03-27