Federated Learning on Riemannian Manifolds with Differential Privacy
Authors: Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra | Published: 2024-04-15
Privacy Protection Method
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
Privacy preserving layer partitioning for Deep Neural Network models
Authors: Kishore Rajasekar, Randolph Loh, Kar Wai Fok, Vrizlynn L. L. Thing | Published: 2024-04-11
Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
Authors: Bishwas Mandal, George Amariucai, Shuangqing Wei | Published: 2024-04-07
Advances in Differential Privacy and Differentially Private Machine Learning
Authors: Saswat Das, Subhankar Mishra | Published: 2024-04-06
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Authors: Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage | Published: 2024-04-05 | Updated: 2024-08-07
AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
Authors: Fei Wei, Ergute Bao, Xiaokui Xiao, Yin Yang, Bolin Ding | Published: 2024-04-02 | Updated: 2024-04-03
Can LLMs get help from other LLMs without revealing private information?
Authors: Florian Hartmann, Duc-Hieu Tran, Peter Kairouz, Victor Cărbune, Blaise Aguera y Arcas | Published: 2024-04-01 | Updated: 2024-04-02
Deciphering the Interplay between Local Differential Privacy, Average Bayesian Privacy, and Maximum Bayesian Privacy
Authors: Xiaojin Zhang, Yulin Fei, Wei Chen | Published: 2024-03-25 | Updated: 2024-04-02