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Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 2,788 papers shown

Title
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 231 431 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 66 7 0 29 Nov 2018
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning Hsin-Pai Cheng P. Yu Haojing Hu Feng Yan Shiyu Li Hai Helen Li Yiran Chen FedML 91 23 0 27 Nov 2018
Generalised Differential Privacy for Text Document Processing Natasha Fernandes Mark Dras Annabelle McIver 88 107 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 81 200 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 38 6 0 25 Nov 2018
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning Muhammad Shayan Clement Fung Chris J. M. Yoon Ivan Beschastnikh FedML 115 82 0 24 Nov 2018
Differential Private Stack Generalization with an Application to Diabetes Prediction Quanming Yao Xiawei Guo James T. Kwok Wei-Wei Tu Yuqiang Chen Wenyuan Dai Qiang Yang 61 19 0 23 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 65 68 0 20 Nov 2018
Private Selection from Private Candidates Jingcheng Liu Kunal Talwar 79 134 0 19 Nov 2018
Private Model Compression via Knowledge Distillation Ji Wang Weidong Bao Lichao Sun Xiaomin Zhu Bokai Cao Philip S. Yu FedML 88 120 0 13 Nov 2018
Boosting Model Performance through Differentially Private Model Aggregation Sophia Collet Robert Dadashi Z. Karam Chang-rui Liu Parinaz Sobhani Yevgeniy Vahlis Ji Chao Zhang FedML 47 1 0 12 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 154 438 0 09 Nov 2018
Mobile Sensor Data Anonymization Mohammad Malekzadeh R. Clegg Andrea Cavallaro Hamed Haddadi 210 214 0 26 Oct 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 127 474 0 10 Oct 2018
Privacy-Preserving Multiparty Learning For Logistic Regression Wei Du Ang Li Qinghua Li 30 16 0 04 Oct 2018
Privacy and Utility Tradeoff in Approximate Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 95 23 0 01 Oct 2018
Privado: Practical and Secure DNN Inference with Enclaves Karan Grover Shruti Tople Shweta Shinde Ranjita Bhagwan Ramachandran Ramjee FedML SILM 82 46 0 01 Oct 2018
Adversarial Attacks and Defences: A Survey Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay Debdeep Mukhopadhyay AAML OOD 167 684 0 28 Sep 2018
Optimal Noise-Adding Mechanism in Additive Differential Privacy Quan Geng Wei Ding Ruiqi Guo Sanjiv Kumar 107 34 0 26 Sep 2018
Understanding Compressive Adversarial Privacy Xiao Chen Peter Kairouz Ram Rajagopal 67 12 0 21 Sep 2018
Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving Juncheng Shen Juzheng Liu Yiran Chen Hai Helen Li FedML 70 1 0 20 Sep 2018
Model-Protected Multi-Task Learning Jian Liang Ziqi Liu Jiayu Zhou Xiaoqian Jiang Changshui Zhang Fei Wang 86 13 0 18 Sep 2018
Déjà Vu: an empirical evaluation of the memorization properties of ConvNets Alexandre Sablayrolles Matthijs Douze Cordelia Schmid Hervé Jégou 54 18 0 17 Sep 2018
Deep Learning in Information Security S. Thaler Vlado Menkovski M. Petković 67 10 0 12 Sep 2018
Learning Rate Adaptation for Federated and Differentially Private Learning A. Koskela Antti Honkela FedML 93 27 0 11 Sep 2018
Deep Learning Towards Mobile Applications Ji Wang Bokai Cao Philip S. Yu Lichao Sun Weidong Bao Xiaomin Zhu HAI 97 99 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 76 196 0 10 Sep 2018
Privacy-Preserving Deep Learning via Weight Transmission L. T. Phong T. Phuong FedML 78 87 0 10 Sep 2018
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent Richeng Jin Xiaofan He H. Dai FedML 80 2 0 08 Sep 2018
Differentially Private Bayesian Inference for Exponential Families G. Bernstein Daniel Sheldon 99 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 149 200 0 30 Aug 2018
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget Jaewoo Lee Daniel Kifer 61 158 0 28 Aug 2018
Privacy-preserving Neural Representations of Text Maximin Coavoux Shashi Narayan Shay B. Cohen AAML 82 118 0 28 Aug 2018
Privacy Amplification by Iteration Vitaly Feldman Ilya Mironov Kunal Talwar Abhradeep Thakurta FedML 120 177 0 20 Aug 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 137 407 0 31 Jul 2018
Security and Privacy Issues in Deep Learning Ho Bae Jaehee Jang Dahuin Jung Hyemi Jang Heonseok Ha Hyungyu Lee Sungroh Yoon SILM MIACV 158 79 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 102 153 0 22 Jul 2018
Efficient Deep Learning on Multi-Source Private Data Nicholas Hynes Raymond Cheng Basel Alomair FedML 96 102 0 17 Jul 2018
Machine Learning with Membership Privacy using Adversarial Regularization Milad Nasr Reza Shokri Amir Houmansadr FedML MIACV 112 478 0 16 Jul 2018
Neural Networks Regularization Through Representation Learning Soufiane Belharbi OOD SSL 39 2 0 13 Jul 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 111 39 0 11 Jul 2018
Differentially Private False Discovery Rate Control Cynthia Dwork Weijie J. Su Li Zhang 84 23 0 11 Jul 2018
Privacy-preserving Machine Learning through Data Obfuscation Tianwei Zhang Zecheng He R. Lee 82 80 0 05 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 97 397 0 04 Jul 2018
How To Backdoor Federated Learning Eugene Bagdasaryan Andreas Veit Yiqing Hua D. Estrin Vitaly Shmatikov SILM FedML 200 1,947 0 02 Jul 2018
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning J. Dhaliwal S. Shintre AAML 59 15 0 27 Jun 2018
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information John C. Duchi Feng Ruan 97 51 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 176 492 0 27 May 2018
AgileNet: Lightweight Dictionary-based Few-shot Learning M. Ghasemzadeh Fang Lin B. Rouhani F. Koushanfar Ke Huang 53 6 0 21 May 2018