Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient
Descent

Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent

11 February 2021

Papers citing "Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent"

19 / 19 papers shown

Title
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses Hao Liang W. Zhang Xinlei He Kaishun He Hong Xing 47 0 0 25 Feb 2025
Understanding the Generalization Error of Markov algorithms through Poissonization Benjamin Dupuis Maxime Haddouche George Deligiannidis Umut Simsekli 47 0 0 11 Feb 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 85 0 0 02 Dec 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 28 7 0 08 Oct 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses Weiwei Kong Mónica Ribero 26 3 0 07 Jul 2024
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping Umut Simsekli Mert Gurbuzbalaban S. Yıldırım Lingjiong Zhu 38 2 0 04 Mar 2024
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 19 2 0 17 Jan 2024
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions T.-H. Hubert Chan Hao Xie Mengshi Zhao 32 1 0 14 Dec 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 39 4 0 19 May 2023
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses S. Asoodeh Mario Díaz 6 6 0 17 May 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Arun Ganesh Daogao Liu Sewoong Oh Abhradeep Thakurta ODL 25 12 0 20 Feb 2023
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 9 1 0 01 Feb 2023
Forget Unlearning: Towards True Data-Deletion in Machine Learning R. Chourasia Neil Shah MU 15 40 0 17 Oct 2022
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling Jason M. Altschuler Kunal Talwar 27 24 0 16 Oct 2022
On the Statistical Complexity of Estimation and Testing under Privacy Constraints Clément Lalanne Aurélien Garivier Rémi Gribonval 25 7 0 05 Oct 2022
Bayesian Estimation of Differential Privacy Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople A. Salem Victor Rühle Andrew J. Paverd Mohammad Naseri Boris Köpf Daniel Jones 6 36 0 10 Jun 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 22 44 0 10 Mar 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 30 99 0 28 Jan 2022
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,814 0 14 Dec 2020