Stochastic Differentially Private and Fair Learning
Authors: Andrew Lowy, Devansh Gupta, Meisam Razaviyayn | Published: 2022-10-17 | Updated: 2023-06-03
プライバシーリスク管理
A General Framework for Auditing Differentially Private Machine Learning
Authors: Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa | Published: 2022-10-16 | Updated: 2023-01-06
Federated Learning with Privacy-Preserving Ensemble Attention Distillation
Authors: Xuan Gong, Liangchen Song, Rishi Vedula, Abhishek Sharma, Meng Zheng, Benjamin Planche, Arun Innanje, Terrence Chen, Junsong Yuan, David Doermann, Ziyan Wu | Published: 2022-10-16
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
Authors: Zhao Song, Yitan Wang, Zheng Yu, Lichen Zhang | Published: 2022-10-15 | Updated: 2023-06-06
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy
Authors: Han Wu, Zilong Zhao, Lydia Y. Chen, Aad van Moorsel | Published: 2022-10-13
Data Privacy and Trustworthy Machine Learning
Authors: Martin Strobel, Reza Shokri | Published: 2022-09-14
Inferring Sensitive Attributes from Model Explanations
Authors: Vasisht Duddu, Antoine Boutet | Published: 2022-08-21 | Updated: 2022-09-07
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
Authors: Wenqiang Ruan, Mingxin Xu, Wenjing Fang, Li Wang, Lei Wang, Weili Han | Published: 2022-08-18
FedPerm: Private and Robust Federated Learning by Parameter Permutation
Authors: Hamid Mozaffari, Virendra J. Marathe, Dave Dice | Published: 2022-08-16
Practical Vertical Federated Learning with Unsupervised Representation Learning
Authors: Zhaomin Wu, Qinbin Li, Bingsheng He | Published: 2022-08-13