Interpretable Complex-Valued Neural Networks for Privacy Protection
Authors: Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang, Jie Ren, Quanshi Zhang | Published: 2019-01-28 | Updated: 2020-01-14
差分プライバシー
PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography
Authors: Maksim Tsikhanovich, Malik Magdon-Ismail, Muhammad Ishaq, Vassilis Zikas | Published: 2019-01-23 | Updated: 2019-04-10
Differentially Private ADMM for Distributed Medical Machine Learning
Authors: Jiahao Ding, Xiaoqi Qin, Wenjun Xu, Yanmin Gong, Chi Zhang, Miao Pan | Published: 2019-01-07 | Updated: 2020-12-09
A Hybrid Approach to Privacy-Preserving Federated Learning
Authors: Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou | Published: 2018-12-07 | Updated: 2019-08-14
Three Tools for Practical Differential Privacy
Authors: Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini | Published: 2018-12-07
When Homomorphic Cryptosystem Meets Differential Privacy: Training Machine Learning Classifier with Privacy Protection
Authors: Xiangyun Tang, Liehuang Zhu, Meng Shen, Xiaojiang Du | Published: 2018-12-06
Differentially Private Data Generative Models
Authors: Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaarfar, Haojin Zhu | Published: 2018-12-06
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation
Authors: Siddharth Garg, Zahra Ghodsi, Carmit Hazay, Yuval Ishai, Antonio Marcedone, Muthuramakrishnan Venkitasubramaniam | Published: 2018-12-04
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Authors: Milad Nasr, Reza Shokri, Amir Houmansadr | Published: 2018-12-03 | Updated: 2020-06-06
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Authors: Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, Hairong Qi | Published: 2018-12-03 | Updated: 2018-12-05