Concentrated Differential Privacy

6 March 2016

Papers citing "Concentrated Differential Privacy"

50 / 270 papers shown

Title
FedPower: Privacy-Preserving Distributed Eigenspace Estimation Xiaoxun Guo Xiang Li Xiangyu Chang Shusen Wang Zhihua Zhang FedML 24 3 0 01 Mar 2021
Lossless Compression of Efficient Private Local Randomizers Vitaly Feldman Kunal Talwar 24 40 0 24 Feb 2021
Federated $f$ -Differential Privacy Qinqing Zheng Shuxiao Chen Qi Long Weijie J. Su FedML 88 55 0 22 Feb 2021
Proactive DP: A Multple Target Optimization Framework for DP-SGD Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Phuong Ha Nguyen 14 0 0 17 Feb 2021
Leveraging Public Data for Practical Private Query Release Terrance Liu G. Vietri Thomas Steinke Jonathan R. Ullman Zhiwei Steven Wu 161 58 0 17 Feb 2021
Differentially Private Quantiles Jennifer Gillenwater Matthew Joseph Alex Kulesza 29 33 0 16 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 44 232 0 12 Feb 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 34 41 0 05 Feb 2021
Local Differential Privacy Is Equivalent to Contraction of $E_γ$ -Divergence S. Asoodeh Maryam Aliakbarpour Flavio du Pin Calmon 14 30 0 02 Feb 2021
Differentially Private SGD with Non-Smooth Losses Puyu Wang Yunwen Lei Yiming Ying Hai Zhang 10 28 0 22 Jan 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 82 216 0 11 Jan 2021
Robustness, Privacy, and Generalization of Adversarial Training Fengxiang He Shaopeng Fu Bohan Wang Dacheng Tao 30 10 0 25 Dec 2020
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 13 157 0 23 Dec 2020
Projection-Free Bandit Optimization with Privacy Guarantees Alina Ene Huy Le Nguyen Adrian Vladu 16 3 0 22 Dec 2020
Recent advances in deep learning theory Fengxiang He Dacheng Tao AI4CE 24 50 0 20 Dec 2020
Differential privacy and noisy confidentiality concepts for European population statistics Fabian Bach 14 6 0 17 Dec 2020
Generating private data with user customization Xiao Chen Thomas Navidi Ram Rajagopal 11 2 0 02 Dec 2020
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds T. Tony Cai Yichen Wang Linjun Zhang FedML 43 20 0 08 Nov 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 40 41 0 19 Oct 2020
Locality Sensitive Hashing with Extended Differential Privacy Natasha Fernandes Yusuke Kawamoto Takao Murakami 32 12 0 19 Oct 2020
Toward Evaluating Re-identification Risks in the Local Privacy Model Takao Murakami Kenta Takahashi AAML 29 10 0 16 Oct 2020
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation Albert Cheu Jonathan R. Ullman FedML 30 21 0 17 Sep 2020
Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms Qiongxiu Li Jaron Skovsted Gundersen Richard Heusdens M. G. Christensen 6 33 0 02 Sep 2020
Imitation Privacy Xun Xian Xinran Wang Mingyi Hong Jie Ding R. Ghanadan 14 3 0 30 Aug 2020
Data Sanitisation Protocols for the Privacy Funnel with Differential Privacy Guarantees Milan Lopuhaä-Zwakenberg Haochen Tong B. Škorić 10 6 0 30 Aug 2020
Individual Privacy Accounting via a Renyi Filter Vitaly Feldman Tijana Zrnic 61 86 0 25 Aug 2020
Three Variants of Differential Privacy: Lossless Conversion and Applications S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar 6 39 0 14 Aug 2020
Differentially Private Accelerated Optimization Algorithms Nurdan Kuru cS. .Ilker Birbil Mert Gurbuzbalaban S. Yıldırım 17 23 0 05 Aug 2020
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms Fengxiang He Bohan Wang Dacheng Tao FedML 25 17 0 18 Jul 2020
A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy Tobias Edwards Benjamin I. P. Rubinstein Zuhe Zhang Sanming Zhou 18 2 0 12 Jul 2020
The Trade-Offs of Private Prediction L. V. D. van der Maaten Awni Y. Hannun 25 22 0 09 Jul 2020
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network Chuan Ma Jun Li Ming Ding Bo Liu Kang Wei J. Weng H. Vincent Poor SyDa 11 19 0 04 Jul 2020
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy Nuria Rodríguez Barroso G. Stipcich Daniel Jiménez-López José Antonio Ruiz-Millán Eugenio Martínez-Cámara Gerardo González-Seco M. V. Luzón M. Veganzones Francisco Herrera 25 100 0 02 Jul 2020
Topology-aware Differential Privacy for Decentralized Image Classification Shangwei Guo Tianwei Zhang Guowen Xu Hanzhou Yu Tao Xiang Yang Liu 22 18 0 14 Jun 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 17 237 0 13 Jun 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 39 115 0 11 Jun 2020
One Step to Efficient Synthetic Data Jordan Awan Zhanrui Cai 28 6 0 03 Jun 2020
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David E. Evans 24 147 0 21 May 2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time Vitaly Feldman Tomer Koren Kunal Talwar 10 203 0 10 May 2020
A Primer on Private Statistics Gautam Kamath Jonathan R. Ullman 38 48 0 30 Apr 2020
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics Mark Cesar Ryan M. Rogers 17 3 0 15 Apr 2020
Assisted Learning: A Framework for Multi-Organization Learning Xun Xian Xinran Wang Jie Ding R. Ghanadan FedML 15 1 0 01 Apr 2020
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion Qinqing Zheng Jinshuo Dong Qi Long Weijie J. Su FedML 9 23 0 10 Mar 2020
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees Aleksei Triastcyn Boi Faltings 12 5 0 02 Mar 2020
Differential Privacy at Risk: Bridging Randomness and Privacy Budget Ashish Dandekar D. Basu S. Bressan 29 8 0 02 Mar 2020
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives R. Arora T. V. Marinov Enayat Ullah 15 1 0 22 Feb 2020
Privately Learning Markov Random Fields Huanyu Zhang Gautam Kamath Janardhan Kulkarni Zhiwei Steven Wu 9 25 0 21 Feb 2020
Guidelines for Implementing and Auditing Differentially Private Systems Daniel Kifer Solomon Messing Aaron Roth Abhradeep Thakurta Danfeng Zhang 14 34 0 10 Feb 2020
DP-CGAN: Differentially Private Synthetic Data and Label Generation Reihaneh Torkzadehmahani Peter Kairouz B. Paten SyDa 17 235 0 27 Jan 2020
A Blockchain-Based Approach for Saving and Tracking Differential-Privacy Cost Yang Zhao Jun Zhao Jiawen Kang Zehang Zhang Dusit Niyato Shuyu Shi 29 24 0 25 Jan 2020