Locally Differentially Private In-Context Learning
Authors: Chunyan Zheng, Keke Sun, Wenhao Zhao, Haibo Zhou, Lixin Jiang, Shaoyang Song, Chunlai Zhou | Published: 2024-05-07 | Updated: 2024-05-08
Privacy Protection Method
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
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Authors: Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg | Published: 2024-05-02
Evaluations of Machine Learning Privacy Defenses are Misleading
Authors: Michael Aerni, Jie Zhang, Florian Tramèr | Published: 2024-04-26 | Updated: 2024-09-05
OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processing
Authors: Songze Li, Mingzhe Liu, Mengqi Chen | Published: 2024-04-17
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
Authors: Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia | Published: 2024-04-17
Federated Learning on Riemannian Manifolds with Differential Privacy
Authors: Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra | Published: 2024-04-15
Privacy at a Price: Exploring its Dual Impact on AI Fairness
Authors: Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni, David Smith, Thierry Rakotoarivelo | Published: 2024-04-15
Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning
Authors: Tanveer Khan, Mindaugas Budzys, Antonis Michalas | Published: 2024-04-14