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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
Model Aggregation via Good-Enough Model Spaces Neel Guha Virginia Smith FedML 20 0 0 20 May 2018
Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability Zitao Li Jean Honorio 92 0 0 19 May 2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle Yu Wang MLT 153 413 0 16 May 2018
Towards Robust and Privacy-preserving Text Representations Yitong Li Timothy Baldwin Trevor Cohn 93 167 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 458 38 0 15 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 218 1,491 0 10 May 2018
Controlling the privacy loss with the input feature maps of the layers in convolutional neural networks Woohyung Chun Sung-Min Hong Junho Huh Inyup Kang PICV 16 0 0 09 May 2018
Learning to Anonymize Faces for Privacy Preserving Action Detection Zhongzheng Ren Yong Jae Lee Michael S. Ryoo CVBM PICV 160 206 0 30 Mar 2018
Privacy Preserving Machine Learning: Threats and Solutions Mohammad Al-Rubaie Jerome Chang 98 338 0 27 Mar 2018
Chiron: Privacy-preserving Machine Learning as a Service T. Hunt Congzheng Song Reza Shokri Vitaly Shmatikov Emmett Witchel 63 201 0 15 Mar 2018
Model-Agnostic Private Learning via Stability Raef Bassily Om Thakkar Abhradeep Thakurta FedML 52 12 0 14 Mar 2018
Deep Learning in Mobile and Wireless Networking: A Survey Chaoyun Zhang P. Patras Hamed Haddadi 152 1,319 0 12 Mar 2018
Generating Artificial Data for Private Deep Learning Aleksei Triastcyn Boi Faltings 102 49 0 08 Mar 2018
I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators Lingxiao Wei Bo Luo Yu LI Yannan Liu Qiang Xu FedML 71 205 0 05 Mar 2018
Learning Anonymized Representations with Adversarial Neural Networks Clément Feutry Pablo Piantanida Yoshua Bengio Pierre Duhamel 93 60 0 26 Feb 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 186 618 0 24 Feb 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos Basel Alomair 322 1,152 0 22 Feb 2018
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation Miles Brundage S. Avin Jack Clark H. Toner P. Eckersley ... Owain Evans Michael Page Joanna J. Bryson Roman V. Yampolskiy Dario Amodei 99 711 0 20 Feb 2018
Differentially Private Generative Adversarial Network Liyang Xie Kaixiang Lin Shu Wang Fei Wang Jiayu Zhou SyDa 124 503 0 19 Feb 2018
Differentially Private Empirical Risk Minimization Revisited: Faster and More General Di Wang Minwei Ye Jinhui Xu 152 272 0 14 Feb 2018
Understanding Membership Inferences on Well-Generalized Learning Models Yunhui Long Vincent Bindschaedler Lei Wang Diyue Bu Wenyuan Xu Haixu Tang Carl A. Gunter Kai Chen MIALM MIACV 106 225 0 13 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 208 940 0 09 Feb 2018
Deep Private-Feature Extraction S. A. Ossia A. Taheri Ali Shahin Shamsabadi Kleomenis Katevas Hamed Haddadi Hamid R. Rabiee 87 96 0 09 Feb 2018
Sometimes You Want to Go Where Everybody Knows your Name Reuben Brasher Nat Roth Justin Wagle 62 0 0 30 Jan 2018
Secure Mobile Crowdsensing with Deep Learning Liang Xiao Donghua Jiang Dongjin Xu Ning An HAI 24 10 0 23 Jan 2018
Differentially Private Releasing via Deep Generative Model (Technical Report) Xinyang Zhang S. Ji Ting Wang SyDa 93 71 0 05 Jan 2018
Differentially Private Matrix Completion Revisited Prateek Jain Om Thakkar Abhradeep Thakurta FedML 91 34 0 28 Dec 2017
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors Gintare Karolina Dziugaite Daniel M. Roy MLT 123 145 0 26 Dec 2017
On Connecting Stochastic Gradient MCMC and Differential Privacy Bai Li Changyou Chen Hao Liu Lawrence Carin 94 38 0 25 Dec 2017
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 237 1,312 0 20 Dec 2017
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 193 1,631 0 19 Dec 2017
Health Data in an Open World C. Culnane Benjamin I. P. Rubinstein Vanessa J. Teague 58 88 0 15 Dec 2017
Deep Learning for IoT Big Data and Streaming Analytics: A Survey M. Mohammadi Ala I. Al-Fuqaha Sameh Sorour Mohsen Guizani 135 1,063 0 09 Dec 2017
Differentially Private Variational Dropout Beyza Ermis A. Cemgil 101 5 0 30 Nov 2017
Differentially Private Dropout Beyza Ermis A. Cemgil SyDa 49 4 0 30 Nov 2017
Ethical Challenges in Data-Driven Dialogue Systems Peter Henderson Koustuv Sinha Nicolas Angelard-Gontier Nan Rosemary Ke G. Fried Ryan J. Lowe Joelle Pineau 83 172 0 24 Nov 2017
CryptoDL: Deep Neural Networks over Encrypted Data Ehsan Hesamifard Hassan Takabi Mehdi Ghasemi 76 382 0 14 Nov 2017
User-centric Composable Services: A New Generation of Personal Data Analytics Jianxin R. Zhao Richard Mortier Jon Crowcroft Liang Wang FedML 55 1 0 25 Oct 2017
Learning Differentially Private Recurrent Language Models H. B. McMahan Daniel Ramage Kunal Talwar Li Zhang FedML 109 127 0 18 Oct 2017
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis Mohammad Malekzadeh R. Clegg Hamed Haddadi 101 72 0 18 Oct 2017
Privacy-Preserving Deep Inference for Rich User Data on The Cloud S. A. Ossia Ali Shahin Shamsabadi A. Taheri Kleomenis Katevas Hamid R. Rabiee Nicholas D. Lane Hamed Haddadi FedML 63 16 0 04 Oct 2017
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 160 280 0 02 Oct 2017
Machine Learning Models that Remember Too Much Congzheng Song Thomas Ristenpart Vitaly Shmatikov VLM 87 525 0 22 Sep 2017
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training Meng Li Liangzhen Lai Naveen Suda Vikas Chandra David Z. Pan 87 17 0 18 Sep 2017
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning Nhathai Phan Xintao Wu Han Hu Dejing Dou 113 190 0 18 Sep 2017
Differentially Private Mixture of Generative Neural Networks G. Ács Luca Melis C. Castelluccia Emiliano De Cristofaro SyDa 108 122 0 13 Sep 2017
PassGAN: A Deep Learning Approach for Password Guessing Briland Hitaj Paolo Gasti G. Ateniese Fernando Perez-Cruz GAN 121 250 0 01 Sep 2017
RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release Thee Chanyaswad Changchang Liu Prateek Mittal 82 5 0 31 Aug 2017
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches Martín Abadi Ulfar Erlingsson Ian Goodfellow H. B. McMahan Ilya Mironov Nicolas Papernot Kunal Talwar Li Zhang 71 47 0 26 Aug 2017
Locally Differentially Private Heavy Hitter Identification Tianhao Wang Ninghui Li S. Jha 95 119 0 22 Aug 2017