Model for Peanuts: Hijacking ML Models without Training Access is Possible
Authors: Mahmoud Ghorbel, Halima Bouzidi, Ioan Marius Bilasco, Ihsen Alouani | Published: 2024-06-03
Membership Inference
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
Authors: Florent Guépin, Nataša Krčo, Matthieu Meeus, Yves-Alexandre de Montjoye | Published: 2024-05-24
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
Authors: Tudor Cebere, Aurélien Bellet, Nicolas Papernot | Published: 2024-05-23 | Updated: 2024-10-14
Information Leakage from Embedding in Large Language Models
Authors: Zhipeng Wan, Anda Cheng, Yinggui Wang, Lei Wang | Published: 2024-05-20 | Updated: 2024-05-22
Learnable Privacy Neurons Localization in Language Models
Authors: Ruizhe Chen, Tianxiang Hu, Yang Feng, Zuozhu Liu | Published: 2024-05-16
PLeak: Prompt Leaking Attacks against Large Language Model Applications
Authors: Bo Hui, Haolin Yuan, Neil Gong, Philippe Burlina, Yinzhi Cao | Published: 2024-05-10 | Updated: 2024-05-14
Model Inversion Robustness: Can Transfer Learning Help?
Authors: Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran, Ngoc-Bao Nguyen, Ngai-Man Cheung | Published: 2024-05-09
IPFed: Identity protected federated learning for user authentication
Authors: Yosuke Kaga, Yusei Suzuki, Kenta Takahashi | Published: 2024-05-07
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
Authors: Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin S. Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger R. Roth | Published: 2024-05-06 | Updated: 2025-03-22
Evaluations of Machine Learning Privacy Defenses are Misleading
Authors: Michael Aerni, Jie Zhang, Florian Tramèr | Published: 2024-04-26 | Updated: 2024-09-05