Privacy Amplification by Iteration

20 August 2018

Papers citing "Privacy Amplification by Iteration"

46 / 46 papers shown

Title
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data Chih-Cheng Rex Yuan Bow-Yaw Wang 52 0 0 30 Apr 2025
How Private is Your Attention? Bridging Privacy with In-Context Learning Soham Bonnerjee Zhen Wei Yeon Anna Asch Sagnik Nandy Promit Ghosal 51 0 0 22 Apr 2025
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses Hao Liang Wenwei Zhang Xinlei He Kaishun He Hong Xing 49 0 0 25 Feb 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 93 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
NetDPSyn: Synthesizing Network Traces under Differential Privacy Danyu Sun Joann Qiongna Chen Chen Gong Tianhao Wang Zhou Li 62 1 0 08 Sep 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
Differentially Private Graph Diffusion with Applications in Personalized PageRanks Rongzhe Wei Eli Chien P. Li 47 5 0 22 Jun 2024
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 28 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
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses S. Asoodeh Mario Díaz 20 6 0 17 May 2023
On the Query Complexity of Training Data Reconstruction in Private Learning Prateeti Mukherjee Satyanarayana V. Lokam 35 0 0 29 Mar 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 99 167 0 01 Mar 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning Edwige Cyffers A. Bellet D. Basu FedML 29 5 0 24 Feb 2023
Straggler-Resilient Differentially-Private Decentralized Learning Yauhen Yakimenka Chung-Wei Weng Hsuan-Yin Lin E. Rosnes J. Kliewer 36 6 0 06 Dec 2022
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 34 0 0 03 Nov 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
Momentum Aggregation for Private Non-convex ERM Hoang Tran Ashok Cutkosky 28 14 0 12 Oct 2022
Differentially Private Deep Learning with ModelMix Hanshen Xiao Jun Wan S. Devadas 29 3 0 07 Oct 2022
Measuring Forgetting of Memorized Training Examples Matthew Jagielski Om Thakkar Florian Tramèr Daphne Ippolito Katherine Lee ... Eric Wallace Shuang Song Abhradeep Thakurta Nicolas Papernot Chiyuan Zhang TDI 75 102 0 30 Jun 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 35 44 0 10 Mar 2022
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms Armando Angrisani Mina Doosti E. Kashefi 24 12 0 07 Mar 2022
Tailoring Gradient Methods for Differentially-Private Distributed Optimization Yongqiang Wang A. Nedić 27 67 0 02 Feb 2022
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith Suriyakumar Om Thakkar Abhradeep Thakurta 21 49 0 01 Dec 2021
Label differential privacy via clustering Hossein Esfandiari Vahab Mirrokni Umar Syed Sergei Vassilvitskii FedML 26 26 0 05 Oct 2021
Random Sampling Plus Fake Data: Multidimensional Frequency Estimates With Local Differential Privacy Héber H. Arcolezi Jean-François Couchot Bechara al Bouna Xiaokui Xiao 25 27 0 15 Sep 2021
Differentially Private Algorithms for Clustering with Stability Assumptions M. Shechner 21 2 0 11 Jun 2021
DP-Image: Differential Privacy for Image Data in Feature Space Hanyu Xue Bo Liu Ming Ding Tianqing Zhu Dayong Ye Li-Na Song Wanlei Zhou 17 33 0 12 Mar 2021
Stability of SGD: Tightness Analysis and Improved Bounds Yikai Zhang Wenjia Zhang Sammy Bald Vamsi Pingali Chao Chen Mayank Goswami MLT 27 36 0 10 Feb 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 82 216 0 11 Jan 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 49 39 0 09 Dec 2020
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 26 64 0 11 Nov 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 24 108 0 07 Jul 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar MLT 24 192 0 12 Jun 2020
Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent Shuang Song Thomas Steinke Om Thakkar Abhradeep Thakurta 35 50 0 11 Jun 2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time Vitaly Feldman Tomer Koren Kunal Talwar 13 203 0 10 May 2020
Differentially Private Set Union Sivakanth Gopi P. Gulhane Janardhan Kulkarni J. Shen Milad Shokouhi Sergey Yekhanin FedML 27 32 0 22 Feb 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 24 38 0 16 Jan 2020
Secure Federated Submodel Learning Chaoyue Niu Fan Wu Shaojie Tang Lifeng Hua Rongfei Jia Chengfei Lv Zhihua Wu Guihai Chen FedML 14 30 0 06 Nov 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 35 122 0 04 Jun 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 42 236 0 07 Mar 2019
Tight Analyses for Non-Smooth Stochastic Gradient Descent Nicholas J. A. Harvey Christopher Liaw Y. Plan Sikander Randhawa 19 137 0 13 Dec 2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 150 420 0 29 Nov 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 33 39 0 11 Jul 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos D. Song 89 1,114 0 22 Feb 2018
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model Brendan Avent Aleksandra Korolova David Zeber Torgeir Hovden B. Livshits FedML 32 98 0 02 May 2017