Composition of Differential Privacy & Privacy Amplification by Subsampling

2 October 2022

Papers citing "Composition of Differential Privacy & Privacy Amplification by Subsampling"

37 / 37 papers shown

Title
A Refreshment Stirred, Not Shaken (III): Can Swapping Be Differentially Private? J. Bailie Ruobin Gong X. Meng 22 0 0 21 Apr 2025
Improving Statistical Privacy by Subsampling Dennis Breutigam Rüdiger Reischuk 24 0 0 15 Apr 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 64 0 0 16 Mar 2025
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 59 1 0 14 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients Clément Lalanne Jean-Michel Loubes David Rodríguez-Vítores FedML 46 0 0 03 Feb 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 87 0 0 28 Jan 2025
Aggregating Data for Optimal and Private Learning Sushant Agarwal Yukti Makhija Rishi Saket A. Raghuveer FedML 73 0 0 28 Nov 2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Thomas Steinke Milad Nasr Arun Ganesh Borja Balle Christopher A. Choquette-Choo Matthew Jagielski Jamie Hayes Abhradeep Thakurta Adam Smith Andreas Terzis 34 7 0 08 Oct 2024
Adaptively Private Next-Token Prediction of Large Language Models James Flemings Meisam Razaviyayn Murali Annavaram 32 0 0 02 Oct 2024
Better Gaussian Mechanism using Correlated Noise Christian Janos Lebeda 44 2 0 13 Aug 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 37 0 0 08 Aug 2024
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 55 1 0 30 Jul 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 40 0 0 22 Jul 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 53 9 0 27 May 2024
Differentially Private Next-Token Prediction of Large Language Models James Flemings Meisam Razaviyayn Murali Annavaram 28 6 0 22 Mar 2024
Privacy-Preserving Instructions for Aligning Large Language Models Da Yu Peter Kairouz Sewoong Oh Zheng Xu 34 17 0 21 Feb 2024
Revisiting Differentially Private Hyper-parameter Tuning Zihang Xiang Tianhao Wang Cheng-Long Wang Di Wang 34 6 0 20 Feb 2024
Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets V. A. Rameshwar Anshoo Tandon Prajjwal Gupta Aditya Vikram Singh Novoneel Chakraborty Abhay Sharma 18 3 0 29 Jan 2024
Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Thomas Steinke Abhradeep Thakurta 30 10 0 24 Oct 2023
Differentially Private Data Generation with Missing Data Shubhankar Mohapatra Jianqiao Zong F. Kerschbaum Xi He SyDa 17 1 0 17 Oct 2023
A Unifying Privacy Analysis Framework for Unknown Domain Algorithms in Differential Privacy Ryan Rogers FedML 25 1 0 17 Sep 2023
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass Minxin Du Xiang Yue Sherman S. M. Chow Tianhao Wang Chenyu Huang Huan Sun SILM 32 58 0 13 Sep 2023
Personalized Privacy Amplification via Importance Sampling Dominik Fay Sebastian Mair Jens Sjölund 57 0 0 05 Jul 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 28 35 0 13 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 35 19 0 23 May 2023
Privacy Auditing with One (1) Training Run Thomas Steinke Milad Nasr Matthew Jagielski 35 77 0 15 May 2023
On Differentially Private Federated Linear Contextual Bandits Xingyu Zhou Sayak Ray Chowdhury FedML 43 15 0 27 Feb 2023
Subsampling Suffices for Adaptive Data Analysis Guy Blanc 30 8 0 17 Feb 2023
One-shot Empirical Privacy Estimation for Federated Learning Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea H. B. McMahan Vinith M. Suriyakumar FedML 27 32 0 06 Feb 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling A. Koskela Tejas D. Kulkarni 36 14 0 27 Jan 2023
Grafting Laplace and Gaussian distributions: A new noise mechanism for differential privacy Gokularam Muthukrishnan Sheetal Kalyani 23 12 0 19 Dec 2022
Lemmas of Differential Privacy Yiyang Huang C. Canonne 31 1 0 21 Nov 2022
Privacy Amplification via Shuffled Check-Ins Seng Pei Liew Satoshi Hasegawa Tsubasa Takahashi FedML 24 0 0 07 Jun 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 25 60 0 16 Feb 2022
Individual Privacy Accounting via a Renyi Filter Vitaly Feldman Tijana Zrnic 59 86 0 25 Aug 2020
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 144 420 0 29 Nov 2018
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 91 278 0 02 Oct 2017