Evaluating Differentially Private Machine Learning in Practice
Authors: Bargav Jayaraman, David Evans | Published: 2019-02-24 | Updated: 2019-08-12
Differential Privacy
Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
Authors: Mehrdad Showkatbakhsh, Can Karakus, Suhas Diggavi | Published: 2019-02-13
Achieving Data Utility-Privacy Tradeoff in Internet of Medical Things: A Machine Learning Approach
Authors: Zhitao Guan, Zefang Lv, Xiaojiang Du, Longfei Wu, Mohsen Guizani | Published: 2019-02-08
Utility Preserving Secure Private Data Release
Authors: Jasjeet Dhaliwal, Geoffrey So, Aleatha Parker-Wood, Melanie Beck | Published: 2019-01-28 | Updated: 2019-03-15
Bayesian Differential Privacy for Machine Learning
Authors: Aleksei Triastcyn, Boi Faltings | Published: 2019-01-28 | Updated: 2020-08-19
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
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