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
メンバーシップ推論
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
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning
Authors: Marlon Tobaben, Hibiki Ito, Joonas Jälkö, Yuan He, Antti Honkela | Published: 2024-02-07 | Updated: 2025-10-06
BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
Authors: Hamed Poursiami, Ihsen Alouani, Maryam Parsa | Published: 2024-02-01 | Updated: 2024-05-07
MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage
Authors: Ying Song, Balaji Palanisamy | Published: 2024-01-23 | Updated: 2025-01-26
Text Embedding Inversion Security for Multilingual Language Models
Authors: Yiyi Chen, Heather Lent, Johannes Bjerva | Published: 2024-01-22 | Updated: 2024-06-05
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them
Authors: Chao Liu, Boxi Chen, Wei Shao, Chris Zhang, Kelvin Wong, Yi Zhang | Published: 2024-01-22 | Updated: 2024-01-27
SODA: Protecting Proprietary Information in On-Device Machine Learning Models
Authors: Akanksha Atrey, Ritwik Sinha, Saayan Mitra, Prashant Shenoy | Published: 2023-12-22
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
Authors: Bang Wu, He Zhang, Xiangwen Yang, Shuo Wang, Minhui Xue, Shirui Pan, Xingliang Yuan | Published: 2023-12-13