The Composition Theorem for Differential Privacy

4 November 2013

Pramod Viswanath

Papers citing "The Composition Theorem for Differential Privacy"

39 / 139 papers shown

Title
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 11 121 0 11 Oct 2021
Combining Differential Privacy and Byzantine Resilience in Distributed SGD R. Guerraoui Nirupam Gupta Rafael Pinot Sébastien Rouault John Stephan FedML 43 4 0 08 Oct 2021
Making the Most of Parallel Composition in Differential Privacy Joshua Smith Hassan Jameel Asghar Gianpaolo Gioiosa Sirine Mrabet Serge Gaspers P. Tyler 23 10 0 19 Sep 2021
Smoothed Differential Privacy Ao Liu Yu-Xiang Wang Lirong Xia 30 0 0 04 Jul 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting Harsha Nori R. Caruana Zhiqi Bu J. Shen Janardhan Kulkarni 33 37 0 17 Jun 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu-Xiang Wang 18 98 0 16 Jun 2021
Cascading Bandit under Differential Privacy Kun Wang Jing Dong Baoxiang Wang Shuai Li Shuo Shao 22 1 0 24 May 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 19 44 0 11 May 2021
Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges Bin Jiang Jianqiang Li Guanghui Yue H. Song 25 117 0 26 Jan 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 65 216 0 11 Jan 2021
Geo-Graph-Indistinguishability: Location Privacy on Road Networks Based on Differential Privacy Shun Takagi Yang Cao Yasuhito Asano Masatoshi Yoshikawa 24 5 0 26 Oct 2020
Learning discrete distributions: user vs item-level privacy Yuhan Liu A. Suresh Felix X. Yu Sanjiv Kumar Michael Riley FedML 25 52 0 27 Jul 2020
Privacy-preserving Artificial Intelligence Techniques in Biomedicine Reihaneh Torkzadehmahani Reza Nasirigerdeh David B. Blumenthal T. Kacprowski M. List ... Harald H. H. W. Schmidt A. Schwalber Christof Tschohl Andrea Wohner Jan Baumbach 21 59 0 22 Jul 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 18 32 0 16 Jun 2020
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David E. Evans 16 147 0 21 May 2020
Differentially Private Assouad, Fano, and Le Cam Jayadev Acharya Ziteng Sun Huanyu Zhang FedML 23 57 0 14 Apr 2020
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale Ryan M. Rogers S. Subramaniam Sean Peng D. Durfee Seunghyun Lee Santosh Kumar Kancha Shraddha Sahay P. Ahammad 24 77 0 14 Feb 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 14 38 0 16 Jan 2020
Assessing differentially private deep learning with Membership Inference Daniel Bernau Philip-William Grassal J. Robl Florian Kerschbaum MIACV FedML 18 23 0 24 Dec 2019
Reviewing and Improving the Gaussian Mechanism for Differential Privacy Jun Zhao Teng Wang Tao Bai Kwok-Yan Lam Zhiying Xu Shuyu Shi Xuebin Ren Xinyu Yang Yang Liu Han Yu 30 30 0 27 Nov 2019
Noiseless Privacy F. Farokhi 11 9 0 29 Oct 2019
Differentially Private SQL with Bounded User Contribution Royce J. Wilson Celia Yuxin Zhang William K. C. Lam Damien Desfontaines Daniel Simmons-Marengo Bryant Gipson 27 147 0 04 Sep 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 9 25 0 28 Jun 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 31 121 0 04 Jun 2019
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy Mehmet Emre Gursoy Acar Tamersoy Stacey Truex Wenqi Wei Ling Liu 19 75 0 15 May 2019
Practical Differentially Private Top- $k$ Selection with Pay-what-you-get Composition D. Durfee Ryan M. Rogers 15 82 0 10 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
Probabilistic Matrix Factorization with Personalized Differential Privacy Shun Zhang Laixiang Liu Zhili Chen Hong Zhong 11 38 0 19 Oct 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu-Xiang Wang Borja Balle S. Kasiviswanathan 14 397 0 31 Jul 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 17 39 0 11 Jul 2018
The Influence of Differential Privacy on Short Term Electric Load Forecasting Günther Eibl Kaibin Bao Philip-William Grassal Daniel Bernau H. Schmeck 16 15 0 06 Jul 2018
cpSGD: Communication-efficient and differentially-private distributed SGD Naman Agarwal A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 8 485 0 27 May 2018
Differentially Private Uniformly Most Powerful Tests for Binomial Data Jordan Awan Aleksandra B. Slavkovic 31 52 0 23 May 2018
Composition Properties of Inferential Privacy for Time-Series Data Shuang Song Kamalika Chaudhuri 24 14 0 10 Jul 2017
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible Kai Zheng Wenlong Mou Liwei Wang 34 41 0 11 Jun 2017
Optimal Differentially Private Mechanisms for Randomised Response N. Holohan D. Leith O. Mason 30 62 0 16 Dec 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 14 5,994 0 01 Jul 2016
Concentrated Differential Privacy Cynthia Dwork G. Rothblum 13 447 0 06 Mar 2016
The Optimal Mechanism in Differential Privacy: Multidimensional Setting Quan Geng Pramod Viswanath 84 122 0 02 Dec 2013