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Near Exact Privacy Amplification for Matrix Mechanisms

8 October 2024

Christopher A. Choquette-Choo

Thomas Steinke

Papers citing "Near Exact Privacy Amplification for Matrix Mechanisms"

25 / 25 papers shown

Title
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD Nikita P. Kalinin Ryan McKenna Jalaj Upadhyay Christoph H. Lampert 112 1 0 17 May 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 125 0 0 16 Mar 2025
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 142 6 0 21 Dec 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yigitsoy Daniel Rueckert Georgios Kaissis 108 0 0 01 Oct 2024
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs H. B. McMahan Zheng Xu Yanxiang Zhang FedML 106 8 0 16 Aug 2024
Continual Counting with Gradual Privacy Expiration Joel Daniel Andersson Monika Henzinger Rasmus Pagh Teresa Anna Steiner Jalaj Upadhyay 85 2 0 06 Jun 2024
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy Krishnamurthy Dvijotham H. B. McMahan Krishna Pillutla Thomas Steinke Abhradeep Thakurta 81 16 0 25 Apr 2024
Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Thomas Steinke Abhradeep Thakurta 77 11 0 24 Oct 2023
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo Krishnamurthy Dvijotham Krishna Pillutla Arun Ganesh Thomas Steinke Abhradeep Thakurta 75 17 0 10 Oct 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training Christopher A. Choquette-Choo Arun Ganesh Ryan McKenna H. B. McMahan Keith Rush Abhradeep Thakurta Zheng Xu FedML 92 41 0 13 Jun 2023
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 82 10 0 17 Apr 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 152 180 0 01 Mar 2023
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning Christopher A. Choquette-Choo H. B. McMahan Keith Rush Abhradeep Thakurta 67 46 0 12 Nov 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 97 66 0 16 Feb 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 286 368 0 25 Sep 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu Wang 55 104 0 16 Jun 2021
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 79 162 0 23 Dec 2020
Privacy Amplification via Random Check-Ins Borja Balle Peter Kairouz H. B. McMahan Om Thakkar Abhradeep Thakurta FedML 80 72 0 13 Jul 2020
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT A. Koskela Hibiki Ito Lukas Prediger Antti Honkela 48 59 0 12 Jun 2020
That which we call private Ulfar Erlingsson Ilya Mironov A. Raghunathan Shuang Song 46 26 0 08 Aug 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 199 430 0 29 Nov 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 87 393 0 04 Jul 2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle Yu Wang MLT 85 411 0 16 May 2018
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,172 0 01 Jul 2016
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 137 1,474 0 06 Mar 2008