Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

6 May 2016

Papers citing "Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds"

50 / 206 papers shown

Title
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 23 10 0 19 Feb 2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams S. Denisov H. B. McMahan J. Rush Adam D. Smith Abhradeep Thakurta FedML 33 60 0 16 Feb 2022
Improved Regret for Differentially Private Exploration in Linear MDP Dung Daniel Ngo G. Vietri Zhiwei Steven Wu 25 8 0 02 Feb 2022
Tailoring Gradient Methods for Differentially-Private Distributed Optimization Yongqiang Wang A. Nedić 24 67 0 02 Feb 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 45 158 0 13 Jan 2022
Differentially-Private Clustering of Easy Instances E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer Eliad Tsfadia 12 22 0 29 Dec 2021
Financial Vision Based Differential Privacy Applications Jun-Hao Chen Yi-Jen Wang Yun-Cheng Tsai Samuel Yen-Chi Chen FedML 21 1 0 28 Dec 2021
The Price of Differential Privacy under Continual Observation Palak Jain Sofya Raskhodnikova Satchit Sivakumar Adam D. Smith 31 51 0 01 Dec 2021
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy Christian Covington Xi He James Honaker Gautam Kamath 30 26 0 27 Oct 2021
An Uncertainty Principle is a Price of Privacy-Preserving Microdata John M. Abowd Robert Ashmead Ryan Cumings-Menon S. Garfinkel Daniel Kifer Philip Leclerc William Sexton Ashley Simpson Christine Task Pavel I Zhuravlev 29 16 0 25 Oct 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 32 14 0 22 Oct 2021
FriendlyCore: Practical Differentially Private Aggregation Eliad Tsfadia E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer 20 33 0 19 Oct 2021
Differentially Private Approximate Quantiles Haim Kaplan Shachar Schnapp Uri Stemmer 26 19 0 11 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 22 122 0 11 Oct 2021
Making the Most of Parallel Composition in Differential Privacy Joshua Smith Hassan Jameel Asghar Gianpaolo Gioiosa Sirine Mrabet Serge Gaspers P. Tyler 31 10 0 19 Sep 2021
Adaptive Control of Differentially Private Linear Quadratic Systems Sayak Ray Chowdhury Xingyu Zhou Ness B. Shroff 38 10 0 26 Aug 2021
Privacy-Aware Rejection Sampling Jordan Awan Vinayak A. Rao 39 7 0 02 Aug 2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges S. Ulukus Salman Avestimehr Michael C. Gastpar S. Jafar Ravi Tandon Chao Tian FedML 33 64 0 30 Jul 2021
$Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity$ Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity Samuel Haney William Sexton Ashwin Machanavajjhala Michael Hay G. Miklau 12 8 0 22 Jul 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 23 71 0 04 Jul 2021
Smoothed Differential Privacy Ao Liu Yu-Xiang Wang Lirong Xia 33 0 0 04 Jul 2021
Faithful Edge Federated Learning: Scalability and Privacy Meng Zhang Ermin Wei R. Berry FedML 26 44 0 30 Jun 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 28 48 0 24 Jun 2021
DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges Ninghui Li Zhikun Zhang Tianhao Wang 21 17 0 24 Jun 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu-Xiang Wang 18 98 0 16 Jun 2021
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods Terrance Liu G. Vietri Zhiwei Steven Wu SyDa 33 61 0 14 Jun 2021
Privately Learning Subspaces Vikrant Singhal Thomas Steinke 27 20 0 28 May 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 22 44 0 11 May 2021
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation Chris Waites Rachel Cummings 19 15 0 25 Mar 2021
DDUO: General-Purpose Dynamic Analysis for Differential Privacy Chiké Abuah Alex Silence David Darais Joseph P. Near 46 12 0 16 Mar 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 35 412 0 14 Mar 2021
Differentially Private Query Release Through Adaptive Projection Sergul Aydore William Brown Michael Kearns K. Kenthapadi Luca Melis Aaron Roth Ankit Siva 45 64 0 11 Mar 2021
IdentityDP: Differential Private Identification Protection for Face Images Yunqian Wen Li-Na Song Bo Liu Ming Ding Rong Xie PICV 45 62 0 02 Mar 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 111 0 25 Feb 2021
No-Regret Algorithms for Private Gaussian Process Bandit Optimization Abhimanyu Dubey 22 13 0 24 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
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 17 125 0 04 Feb 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
Learning Differentially Private Mechanisms Subhajit Roy Justin Hsu Aws Albarghouthi FedML SyDa 38 17 0 04 Jan 2021
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms Zeyu Ding Yuxin Wang Yingtai Xiao Guanhong Wang Danfeng Zhang Daniel Kifer 31 6 0 02 Dec 2020
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data Di Wang Hanshen Xiao S. Devadas Jinhui Xu 29 55 0 21 Oct 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 40 42 0 19 Oct 2020
An Information Theoretic approach to Post Randomization Methods under Differential Privacy Fadhel Ayed Marco Battiston F. Camerlenghi 32 2 0 23 Sep 2020
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning Rui Hu Yanmin Gong FedML 28 63 0 11 Sep 2020
Stochastic Adaptive Line Search for Differentially Private Optimization Chen Chen Jaewoo Lee 22 14 0 18 Aug 2020
Differentially Private Accelerated Optimization Algorithms Nurdan Kuru cS. .Ilker Birbil Mert Gurbuzbalaban S. Yıldırım 25 23 0 05 Aug 2020
New Oracle-Efficient Algorithms for Private Synthetic Data Release G. Vietri Grace Tian Mark Bun Thomas Steinke Zhiwei Steven Wu SyDa 88 75 0 10 Jul 2020
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi MU 17 250 0 06 Jul 2020
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems Sixu Hu Yuan N. Li Xu Liu Yue Liu Zhaomin Wu Bingsheng He FedML 18 53 0 14 Jun 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 39 115 0 11 Jun 2020