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
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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
Low-Cost High-Power Membership Inference Attacks
Authors: Sajjad Zarifzadeh, Philippe Liu, Reza Shokri | Published: 2023-12-06 | Updated: 2024-06-12
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
Authors: Soumya Banerjee, Sandip Roy, Sayyed Farid Ahamed, Devin Quinn, Marc Vucovich, Dhruv Nandakumar, Kevin Choi, Abdul Rahman, Edward Bowen, Sachin Shetty | Published: 2023-11-28
PrivateLoRA For Efficient Privacy Preserving LLM
Authors: Yiming Wang, Yu Lin, Xiaodong Zeng, Guannan Zhang | Published: 2023-11-23