Scalable Private Learning with PATE

24 February 2018

Papers citing "Scalable Private Learning with PATE"

38 / 138 papers shown

Title
Practical One-Shot Federated Learning for Cross-Silo Setting Qinbin Li Bingsheng He D. Song FedML 16 113 0 02 Oct 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 34 125 0 05 Aug 2020
Anonymizing Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash MIACV 9 5 0 26 Jul 2020
Private Post-GAN Boosting Marcel Neunhoeffer Zhiwei Steven Wu Cynthia Dwork 116 29 0 23 Jul 2020
Probabilistic Jacobian-based Saliency Maps Attacks Théo Combey António Loison Maxime Faucher H. Hajri AAML 16 19 0 12 Jul 2020
The Trade-Offs of Private Prediction L. V. D. van der Maaten Awni Y. Hannun 20 22 0 09 Jul 2020
Reducing Risk of Model Inversion Using Privacy-Guided Training Abigail Goldsteen Gilad Ezov Ariel Farkash 17 4 0 29 Jun 2020
SPEED: Secure, PrivatE, and Efficient Deep learning Arnaud Grivet Sébert Rafael Pinot Martin Zuber Cédric Gouy-Pailler Renaud Sirdey FedML 15 20 0 16 Jun 2020
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy Sicong Liu Junzhao Du Anshumali Shrivastava Lin Zhong 36 14 0 08 Jun 2020
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins? Jeremy Georges-Filteau Elisa Cirillo SyDa AI4CE 36 17 0 27 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 25 83 0 18 May 2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time Vitaly Feldman Tomer Koren Kunal Talwar 8 203 0 10 May 2020
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 28 217 0 05 May 2020
A Review of Privacy-preserving Federated Learning for the Internet-of-Things Christopher Briggs Zhong Fan Péter András 25 14 0 24 Apr 2020
Private Query Release Assisted by Public Data Raef Bassily Albert Cheu Shay Moran Aleksandar Nikolov Jonathan R. Ullman Zhiwei Steven Wu 76 47 0 23 Apr 2020
Differentially Private Deep Learning with Smooth Sensitivity Lichao Sun Yingbo Zhou Philip S. Yu Caiming Xiong FedML 18 9 0 01 Mar 2020
Understanding and Improving Knowledge Distillation Jiaxi Tang Rakesh Shivanna Zhe Zhao Dong Lin Anima Singh Ed H. Chi Sagar Jain 27 129 0 10 Feb 2020
Radioactive data: tracing through training Alexandre Sablayrolles Matthijs Douze Cordelia Schmid Hervé Jégou 35 74 0 03 Feb 2020
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning Zhiying Xu Shuyu Shi A. Liu Jun Zhao Lin Chen FedML 24 36 0 19 Dec 2019
Private Federated Learning with Domain Adaptation Daniel W. Peterson Pallika H. Kanani Virendra J. Marathe FedML 13 81 0 13 Dec 2019
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning Runhua Xu Nathalie Baracaldo Yi Zhou Ali Anwar Heiko Ludwig FedML 14 287 0 12 Dec 2019
Federated Learning with Bayesian Differential Privacy Aleksei Triastcyn Boi Faltings FedML 13 172 0 22 Nov 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM Bao Wang Quanquan Gu M. Boedihardjo Farzin Barekat Stanley J. Osher 11 25 0 28 Jun 2019
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators Yunhui Long Wei Ping Zhuolin Yang B. Kailkhura Aston Zhang C.A. Gunter Bo-wen Li 14 72 0 21 Jun 2019
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation Mark Bun Thomas Steinke 47 74 0 06 Jun 2019
Differentially Private Learning with Adaptive Clipping Galen Andrew Om Thakkar H. B. McMahan Swaroop Ramaswamy FedML 19 330 0 09 May 2019
A Hybrid Approach to Privacy-Preserving Federated Learning Stacey Truex Nathalie Baracaldo Ali Anwar Thomas Steinke Heiko Ludwig Rui Zhang Yi Zhou FedML 17 882 0 07 Dec 2018
Differentially Private Data Generative Models Qingrong Chen Chong Xiang Minhui Xue Bo-wen Li Nikita Borisov Dali Kaafar Haojin Zhu SyDa AAML 15 79 0 06 Dec 2018
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning Milad Nasr Reza Shokri Amir Houmansadr FedML MIACV AAML 13 243 0 03 Dec 2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference Edward Chou Josh Beal Daniel Levy Serena Yeung Albert Haque Li Fei-Fei 23 198 0 25 Nov 2018
A Fully Private Pipeline for Deep Learning on Electronic Health Records Edward Chou Thao Nguyen Josh Beal Albert Haque Li Fei-Fei SyDa FedML 13 6 0 25 Nov 2018
Private Machine Learning in TensorFlow using Secure Computation Morten Dahl Jason V. Mancuso Yann Dupis Ben Decoste Morgan Giraud Ian Livingstone Justin Patriquin Gavin Uhma FedML 13 75 0 18 Oct 2018
Privacy Amplification by Iteration Vitaly Feldman Ilya Mironov Kunal Talwar Abhradeep Thakurta FedML 16 170 0 20 Aug 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 24 39 0 11 Jul 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 49 1,454 0 10 May 2018
Privacy-preserving Prediction Cynthia Dwork Vitaly Feldman 25 90 0 27 Mar 2018
Generating Artificial Data for Private Deep Learning Aleksei Triastcyn Boi Faltings 21 48 0 08 Mar 2018
On Connecting Stochastic Gradient MCMC and Differential Privacy Bai Li Changyou Chen Hao Liu Lawrence Carin 41 38 0 25 Dec 2017