Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

4 July 2018

Gilles Barthe

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

50 / 88 papers shown

Title
Gaussian Differential Private Bootstrap by Subsampling Holger Dette Carina Graw 38 0 0 02 May 2025
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 38 6 0 08 Oct 2024
Differentially Private Bilevel Optimization Guy Kornowski 142 0 0 29 Sep 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 31 0 0 31 Jul 2024
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 58 1 0 30 Jul 2024
Delving into Differentially Private Transformer Youlong Ding Xueyang Wu Yining Meng Yonggang Luo Hao Wang Weike Pan 39 5 0 28 May 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
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 56 5 0 10 May 2024
Budget Recycling Differential Privacy Bo Jiang Jian Du Sagar Shamar Qiang Yan 18 1 0 18 Mar 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 31 5 0 29 Jan 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 47 18 0 09 Jan 2024
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST) Xingyuan Zhao Fang Liu 30 0 0 20 Dec 2023
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
All Rivers Run to the Sea: Private Learning with Asymmetric Flows Yue Niu Ramy E. Ali Saurav Prakash Salman Avestimehr FedML 28 2 0 05 Dec 2023
DP-NMT: Scalable Differentially-Private Machine Translation Timour Igamberdiev Doan Nam Long Vu Felix Künnecke Zhuo Yu Jannik Holmer Ivan Habernal 31 7 0 24 Nov 2023
Communication Efficient Private Federated Learning Using Dithering Burak Hasircioglu Deniz Gunduz FedML 45 7 0 14 Sep 2023
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 37 1 0 04 Sep 2023
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks Shijie Liu Andrew C. Cullen Paul Montague S. Erfani Benjamin I. P. Rubinstein AAML 23 3 0 15 Aug 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 35 11 0 08 Jul 2023
Personalized Privacy Amplification via Importance Sampling Dominik Fay Sebastian Mair Jens Sjölund 57 0 0 05 Jul 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 37 2 0 18 May 2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy Yi Shi Kang Wei Li Shen Yingqi Liu Xueqian Wang Bo Yuan Dacheng Tao FedML 35 2 0 01 May 2023
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy Zhenxiao Zhang Yuanxiong Guo Yuguang Fang Yanmin Gong 33 4 0 15 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 26 9 0 11 Apr 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 94 167 0 01 Mar 2023
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy R. Saha Mohamed Seif M. Yemini Andrea J. Goldsmith H. Vincent Poor FedML 21 2 0 28 Feb 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
Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science Jayshree Sarathy Sophia Song Audrey Haque Tania Schlatter Salil P. Vadhan 25 23 0 23 Feb 2023
Differentially Private Optimization for Smooth Nonconvex ERM Changyu Gao Stephen J. Wright 16 6 0 09 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 30 18 0 22 Jan 2023
Differentially Private Federated Clustering over Non-IID Data Yiwei Li Shuai Wang Chong-Yung Chi Tony Q. S. Quek FedML 33 12 0 03 Jan 2023
Training Differentially Private Graph Neural Networks with Random Walk Sampling Morgane Ayle Jan Schuchardt Lukas Gosch Daniel Zügner Stephan Günnemann FedML 21 6 0 02 Jan 2023
ReSQueing Parallel and Private Stochastic Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Y. Lee Daogao Liu Aaron Sidford Kevin Tian FedML 22 12 0 01 Jan 2023
Social-Aware Clustered Federated Learning with Customized Privacy Preservation Yuntao Wang Zhou Su Yanghe Pan Tom H. Luan Ruidong Li Shui Yu FedML 26 18 0 25 Dec 2022
Straggler-Resilient Differentially-Private Decentralized Learning Yauhen Yakimenka Chung-Wei Weng Hsuan-Yin Lin E. Rosnes J. Kliewer 29 6 0 06 Dec 2022
Privacy-preserving Non-negative Matrix Factorization with Outliers Swapnil Saha H. Imtiaz PICV 21 3 0 02 Nov 2022
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 32 2 0 28 Oct 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 46 20 0 18 Oct 2022
Momentum Aggregation for Private Non-convex ERM Hoang Tran Ashok Cutkosky 26 14 0 12 Oct 2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 34 9 0 12 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 61 49 0 02 Oct 2022
On the Choice of Databases in Differential Privacy Composition Valentin Hartmann Vincent Bindschaedler Robert West 26 0 0 27 Sep 2022
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification Sajani Vithana S. Ulukus FedML 18 19 0 09 Sep 2022
The Saddle-Point Accountant for Differential Privacy Wael Alghamdi S. Asoodeh Flavio du Pin Calmon Juan Felipe Gomez O. Kosut Lalitha Sankar Fei Wei 17 7 0 20 Aug 2022
When Does Differentially Private Learning Not Suffer in High Dimensions? Xuechen Li Daogao Liu Tatsunori Hashimoto Huseyin A. Inan Janardhan Kulkarni Y. Lee Abhradeep Thakurta 34 58 0 01 Jul 2022
Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling Gamal Elkoumy Marlon Dumas 17 6 0 27 Jun 2022
Analytical Composition of Differential Privacy via the Edgeworth Accountant Hua Wang Sheng-yang Gao Huanyu Zhang Milan Shen Weijie J. Su FedML 28 21 0 09 Jun 2022
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Yi Ma T. V. Marinov Tong Zhang 19 8 0 03 Jun 2022
On the Privacy Properties of GAN-generated Samples Zinan Lin Vyas Sekar Giulia Fanti PICV 21 26 0 03 Jun 2022
Differentially Private Shapley Values for Data Evaluation Lauren Watson R. Andreeva Hao Yang Rik Sarkar TDI FAtt FedML 18 6 0 01 Jun 2022