Optimal Accounting of Differential Privacy via Characteristic Function

16 June 2021

Papers citing "Optimal Accounting of Differential Privacy via Characteristic Function"

50 / 71 papers shown

Title
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models Antti Koskela Mohamed Seif Andrea J. Goldsmith 36 1 0 09 May 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 69 0 0 16 Mar 2025
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 66 1 0 14 Mar 2025
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 65 0 0 13 Mar 2025
$Approximate Differential Privacy of the $\ell_2$ Mechanism$ Approximate Differential Privacy of the $\ell_2$ Mechanism Matthew Joseph Alex Kulesza Alexander Yu 39 0 0 21 Feb 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 89 0 0 28 Jan 2025
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 90 4 0 21 Dec 2024
Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Bo Jiang Wanrong Zhang Donghang Lu Jian Du Sagar Sharma Qiang Yan 90 0 0 13 Dec 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling Lynn Chua Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 46 5 0 06 Nov 2024
The 2020 United States Decennial Census Is More Private Than You (Might) Think Buxin Su Weijie J. Su Chendi Wang 41 3 0 11 Oct 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 47 7 0 08 Oct 2024
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem Weijie J. Su 33 1 0 14 Sep 2024
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement Jeremiah Birrell Reza Ebrahimi R. Behnia Jason L. Pacheco 51 0 0 19 Aug 2024
Better Gaussian Mechanism using Correlated Noise Christian Janos Lebeda 49 2 0 13 Aug 2024
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong FedML 49 3 0 20 Jul 2024
Fine-Tuning Large Language Models with User-Level Differential Privacy Zachary Charles Arun Ganesh Ryan McKenna H. B. McMahan Nicole Mitchell Krishna Pillutla Keith Rush 44 11 0 10 Jul 2024
Attack-Aware Noise Calibration for Differential Privacy B. Kulynych Juan Felipe Gomez G. Kaissis Flavio du Pin Calmon Carmela Troncoso 62 6 0 02 Jul 2024
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy Georgios Kaissis Stefan Kolek Borja Balle Jamie Hayes Daniel Rueckert 52 4 0 13 Jun 2024
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models Qin Yang Meisam Mohammady Han Wang Ali Payani Ashish Kundu Kai Shu Yan Yan Yuan Hong 44 0 0 29 May 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 58 9 0 27 May 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning Chendi Wang Yuqing Zhu Weijie J. Su Yu Wang AAML 63 4 0 14 May 2024
Secure Aggregation is Not Private Against Membership Inference Attacks K. Ngo Johan Ostman Giuseppe Durisi Alexandre Graell i Amat FedML 40 2 0 26 Mar 2024
How Private are DP-SGD Implementations? Lynn Chua Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 48 12 0 26 Mar 2024
Budget Recycling Differential Privacy Bo Jiang Jian Du Sagar Shamar Qiang Yan 34 1 0 18 Mar 2024
Privacy Profiles for Private Selection Antti Koskela Rachel Redberg Yu-Xiang Wang 41 1 0 09 Feb 2024
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation Ossi Raisa Joonas Jälkö Antti Honkela 34 6 0 06 Feb 2024
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 31 2 0 17 Jan 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 50 18 0 09 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners Rachel Redberg Antti Koskela Yu-Xiang Wang 83 5 0 31 Dec 2023
Zero redundancy distributed learning with differential privacy Zhiqi Bu Justin Chiu Ruixuan Liu Sheng Zha George Karypis 56 8 0 20 Nov 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 37 2 0 14 Nov 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$ -Differential Privacy Chendi Wang Buxin Su Jiayuan Ye Reza Shokri Weijie J. Su FedML 26 10 0 30 Oct 2023
On the accuracy and efficiency of group-wise clipping in differentially private optimization Zhiqi Bu Ruixuan Liu Yu Wang Sheng Zha George Karypis VLM 40 4 0 30 Oct 2023
Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Thomas Steinke Abhradeep Thakurta 35 10 0 24 Oct 2023
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy Yingyu Lin Yian Ma Yu-Xiang Wang Rachel Redberg Zhiqi Bu 41 4 0 23 Oct 2023
An In-Depth Examination of Requirements for Disclosure Risk Assessment Ron S. Jarmin John M. Abowd Robert Ashmead Ryan Cumings-Menon N. Goldschlag ... Jerome P. Reiter Rolando A. Rodríguez Ian M. Schmutte V. Velkoff Pavel I Zhuravlev 37 9 0 13 Oct 2023
DP-Auditorium: a Large Scale Library for Auditing Differential Privacy William Kong Andrés Munoz Medina Mónica Ribero Umar Syed 40 2 0 10 Jul 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 48 11 0 08 Jul 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
Privacy Auditing with One (1) Training Run Thomas Steinke Milad Nasr Matthew Jagielski 58 77 0 15 May 2023
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 41 9 0 17 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 33 9 0 11 Apr 2023
Considerations on the Theory of Training Models with Differential Privacy Marten van Dijk Phuong Ha Nguyen FedML 38 2 0 08 Mar 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 40 39 0 14 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 43 9 0 30 Jan 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling A. Koskela Tejas D. Kulkarni 49 14 0 27 Jan 2023
Generalizing DP-SGD with Shuffling and Batch Clipping Marten van Dijk Phuong Ha Nguyen Toan N. Nguyen Lam M. Nguyen 28 1 0 12 Dec 2022
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens Georgios Kaissis Alexander Ziller Stefan Kolek Martinez de Azagra Daniel Rueckert 12 0 0 24 Oct 2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 36 9 0 12 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 79 50 0 02 Oct 2022