Efficient Deep Learning on Multi-Source Private Data
Authors: Nick Hynes, Raymond Cheng, Dawn Song | Published: 2018-07-17
Differential Privacy
Differentially-Private “Draw and Discard” Machine Learning
Authors: Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng | Published: 2018-07-11 | Updated: 2018-10-10
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Authors: Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin | Published: 2018-07-02 | Updated: 2019-10-26
Privacy Preserving Analytics on Distributed Medical Data
Authors: Marina Blanton, Ah Reum Kang, Subhadeep Karan, Jaroslaw Zola | Published: 2018-06-18
Differentially Private Confidence Intervals for Empirical Risk Minimization
Authors: Yue Wang, Daniel Kifer, Jaewoo Lee | Published: 2018-04-11
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
Authors: Pierre Dellenbach, Aurélien Bellet, Jan Ramon | Published: 2018-03-27
Chiron: Privacy-preserving Machine Learning as a Service
Authors: Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, Emmett Witchel | Published: 2018-03-15
Generating Artificial Data for Private Deep Learning
Authors: Aleksei Triastcyn, Boi Faltings | Published: 2018-03-08 | Updated: 2019-04-28
Scalable Private Learning with PATE
Authors: Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson | Published: 2018-02-24
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Authors: Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, Dawn Song | Published: 2018-02-22 | Updated: 2019-07-16