v1v2 (latest)

Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

4 July 2018

Borja Balle

Gilles Barthe

Marco Gaboardi

ArXiv (abs)PDF HTML

Papers citing "Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences"

25 / 25 papers shown

Title
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 123 9 0 08 Oct 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 85 0 0 31 Jul 2024
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 114 1 0 30 Jul 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 114 10 0 27 May 2024
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation Jie Xu Karthikeyan P. Saravanan Rogier van Dalen Haaris Mehmood David Tuckey Mete Ozay 154 8 0 10 May 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 180 25 0 09 Jan 2024
Personalized Privacy Amplification via Importance Sampling Dominik Fay Sebastian Mair Jens Sjölund 115 0 0 05 Jul 2023
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation Mohamed Seif Wei-Ting Chang Ravi Tandon FedML 89 46 0 02 Mar 2021
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 85 401 0 31 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
Renyi Differential Privacy Ilya Mironov 91 1,268 0 24 Feb 2017
Variational Bayes In Private Settings (VIPS) Mijung Park James R. Foulds Kamalika Chaudhuri Max Welling 66 42 0 01 Nov 2016
Differentially Private Variational Inference for Non-conjugate Models Hibiki Ito O. Dikmen Antti Honkela FedML 65 48 0 27 Oct 2016
Private Topic Modeling Mijung Park James R. Foulds Kamalika Chaudhuri Max Welling 43 12 0 14 Sep 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,162 0 01 Jul 2016
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds Mark Bun Thomas Steinke 92 840 0 06 May 2016
Concentrated Differential Privacy Cynthia Dwork G. Rothblum 80 453 0 06 Mar 2016
Proving Differential Privacy via Probabilistic Couplings Gilles Barthe Marco Gaboardi B. Grégoire Justin Hsu Pierre-Yves Strub 95 103 0 19 Jan 2016
Differentially Private Release and Learning of Threshold Functions Mark Bun Kobbi Nissim Uri Stemmer Salil P. Vadhan 83 198 0 28 Apr 2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu Wang S. Fienberg Alex Smola 86 249 0 26 Feb 2015
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle Yu Wang Jing Lei S. Fienberg 80 103 0 23 Feb 2015
Characterizing the Sample Complexity of Private Learners A. Beimel Kobbi Nissim Uri Stemmer 81 80 0 10 Feb 2014
The Composition Theorem for Differential Privacy Peter Kairouz Sewoong Oh Pramod Viswanath 145 685 0 04 Nov 2013
On Sampling, Anonymization, and Differential Privacy: Or, k-Anonymization Meets Differential Privacy Ninghui Li Wahbeh H. Qardaji D. Su 126 282 0 13 Jan 2011
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 137 1,474 0 06 Mar 2008