Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,131 papers shown

Title
SecureBoost: A Lossless Federated Learning Framework Kewei Cheng Tao Fan Yilun Jin Yang Liu Tianjian Chen Dimitrios Papadopoulos Qiang Yang FedML 25 578 0 25 Jan 2019
LEP-CNN: A Lightweight Edge Device Assisted Privacy-preserving CNN Inference Solution for IoT Yifan Tian Jiawei Yuan Shucheng Yu Yantian Hou 21 9 0 14 Jan 2019
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data Lorenzo Frigerio Anderson Santana de Oliveira L. Gomez Patrick Duverger SyDa AI4TS 31 110 0 08 Jan 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 25 74 0 08 Jan 2019
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants Lingchen Zhao Qian Wang Qin Zou Yan Zhang Yanjiao Chen FedML 18 9 0 25 Dec 2018
A General Approach to Adding Differential Privacy to Iterative Training Procedures H. B. McMahan Galen Andrew Ulfar Erlingsson Steve Chien Ilya Mironov Nicolas Papernot Peter Kairouz 19 192 0 15 Dec 2018
No Peek: A Survey of private distributed deep learning Praneeth Vepakomma Tristan Swedish Ramesh Raskar O. Gupta Abhimanyu Dubey SyDa FedML 30 100 0 08 Dec 2018
A Hybrid Approach to Privacy-Preserving Federated Learning Stacey Truex Nathalie Baracaldo Ali Anwar Thomas Steinke Heiko Ludwig Rui Zhang Yi Zhou FedML 31 884 0 07 Dec 2018
Three Tools for Practical Differential Privacy K. V. D. Veen Ruben Seggers Peter Bloem Giorgio Patrini 19 39 0 07 Dec 2018
Differentially Private Data Generative Models Qingrong Chen Chong Xiang Minhui Xue Bo Li Nikita Borisov Dali Kaafar Haojin Zhu SyDa AAML 17 79 0 06 Dec 2018
Privacy-Preserving Distributed Deep Learning for Clinical Data Brett K. Beaulieu-Jones W. Yuan S. G. Finlayson Zhiwei Steven Wu OOD FedML 22 46 0 04 Dec 2018
Protection Against Reconstruction and Its Applications in Private Federated Learning Abhishek Bhowmick John C. Duchi Julien Freudiger Gaurav Kapoor Ryan M. Rogers FedML 27 357 0 03 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 13 244 0 03 Dec 2018
Split learning for health: Distributed deep learning without sharing raw patient data Praneeth Vepakomma O. Gupta Tristan Swedish Ramesh Raskar FedML 63 692 0 03 Dec 2018
Deep Learning Application in Security and Privacy -- Theory and Practice: A Position Paper Julia A. Meister Raja Naeem Akram K. Markantonakis 26 0 0 01 Dec 2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 150 420 0 29 Nov 2018
MOBIUS: Model-Oblivious Binarized Neural Networks Hiromasa Kitai Jason Paul Cruz Naoto Yanai Naohisa Nishida Tatsumi Oba Yuji Unagami Tadanori Teruya Nuttapong Attrapadung Takahiro Matsuda Goichiro Hanaoka 24 7 0 29 Nov 2018
Generalised Differential Privacy for Text Document Processing Natasha Fernandes Mark Dras Annabelle McIver 27 107 0 26 Nov 2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference Edward Chou Josh Beal Daniel Levy Serena Yeung Albert Haque Li Fei-Fei 32 198 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 16 6 0 25 Nov 2018
FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions Shaohua Li Kaiping Xue Chenkai Ding Xindi Gao David S. L. Wei Tao Wan F. Wu 30 67 0 20 Nov 2018
Private Model Compression via Knowledge Distillation Ji Wang Weidong Bao Lichao Sun Xiaomin Zhu Bokai Cao Philip S. Yu FedML 14 116 0 13 Nov 2018
A generic framework for privacy preserving deep learning Wenbo Guo Yunzhe Tao Morten Dahl Sui Huang Masashi Sugiyama Daniel Rueckert Lin Lin FedML 29 428 0 09 Nov 2018
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation Micah J. Sheller G. A. Reina Brandon Edwards Jason Martin Spyridon Bakas FedML 29 457 0 10 Oct 2018
Privacy and Utility Tradeoff in Approximate Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 24 23 0 01 Oct 2018
Optimal Noise-Adding Mechanism in Additive Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 21 34 0 26 Sep 2018
Understanding Compressive Adversarial Privacy Xiao Chen Peter Kairouz Ram Rajagopal 17 11 0 21 Sep 2018
Deep Learning Towards Mobile Applications Ji Wang Bokai Cao Philip S. Yu Lichao Sun Weidong Bao Xiaomin Zhu HAI 32 98 0 10 Sep 2018
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud Ji Wang Jianguo Zhang Weidong Bao Xiaomin Zhu Bokai Cao Philip S. Yu 29 193 0 10 Sep 2018
Differentially Private Bayesian Inference for Exponential Families G. Bernstein Daniel Sheldon 36 48 0 06 Sep 2018
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy Zonghao Huang Rui Hu Yuanxiong Guo Eric Chan-Tin Yanmin Gong FedML 11 194 0 30 Aug 2018
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget Jaewoo Lee Daniel Kifer 22 156 0 28 Aug 2018
Privacy Amplification by Iteration Vitaly Feldman Ilya Mironov Kunal Talwar Abhradeep Thakurta FedML 26 170 0 20 Aug 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 14 398 0 31 Jul 2018
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study Zhenyu Wu Zhangyang Wang Zhaowen Wang Hailin Jin AAML PICV 33 153 0 22 Jul 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 33 39 0 11 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 32 378 0 04 Jul 2018
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information John C. Duchi Feng Ruan 25 50 0 14 Jun 2018
cpSGD: Communication-efficient and differentially-private distributed SGD Naman Agarwal A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 28 486 0 27 May 2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle Yu Wang MLT 24 390 0 16 May 2018
Towards Robust and Privacy-preserving Text Representations Yitong Li Timothy Baldwin Trevor Cohn 19 165 0 16 May 2018
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning Tribhuvanesh Orekondy Seong Joon Oh Yang Zhang Bernt Schiele Mario Fritz PICV FedML 359 37 0 15 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 93 1,455 0 10 May 2018
Deep Learning in Mobile and Wireless Networking: A Survey Chaoyun Zhang P. Patras Hamed Haddadi 50 1,306 0 12 Mar 2018
Generating Artificial Data for Private Deep Learning Aleksei Triastcyn Boi Faltings 21 48 0 08 Mar 2018
Learning Anonymized Representations with Adversarial Neural Networks Clément Feutry Pablo Piantanida Yoshua Bengio Pierre Duhamel 19 59 0 26 Feb 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 41 607 0 24 Feb 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos D. Song 89 1,117 0 22 Feb 2018
Differentially Private Generative Adversarial Network Liyang Xie Kaixiang Lin Shu Wang Fei Wang Jiayu Zhou SyDa 24 491 0 19 Feb 2018
Differentially Private Empirical Risk Minimization Revisited: Faster and More General Di Wang Minwei Ye Jinhui Xu 26 268 0 14 Feb 2018