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"

22 / 22 papers shown

Title
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses Hao Liang Feiyu Xiong 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 87 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 34 7 0 08 Oct 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses Weiwei Kong Mónica Ribero 37 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 26 2 0 17 Jan 2024
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions T.-H. Hubert Chan Hao Xie Mengshi Zhao 37 1 0 14 Dec 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 41 4 0 19 May 2023
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses S. Asoodeh Mario Díaz 20 6 0 17 May 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 96 167 0 01 Mar 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Arun Ganesh Daogao Liu Sewoong Oh Abhradeep Thakurta ODL 27 12 0 20 Feb 2023
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 19 1 0 01 Feb 2023
Lower Bounds for Rényi Differential Privacy in a Black-Box Setting T. Kutta Önder Askin Martin Dunsche 25 4 0 09 Dec 2022
Forget Unlearning: Towards True Data-Deletion in Machine Learning R. Chourasia Neil Shah MU 21 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 38 24 0 16 Oct 2022
On the Statistical Complexity of Estimation and Testing under Privacy Constraints Clément Lalanne Aurélien Garivier Rémi Gribonval 27 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 25 36 0 10 Jun 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 35 44 0 10 Mar 2022
What Does it Mean for a Language Model to Preserve Privacy? Hannah Brown Katherine Lee Fatemehsadat Mireshghallah Reza Shokri Florian Tramèr PILM 50 232 0 11 Feb 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 36 100 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 1,824 0 14 Dec 2020