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Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT

12 June 2020

Papers citing "Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT"

40 / 40 papers shown

Title
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 104 0 0 13 Mar 2025
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 143 10 0 08 Oct 2024
Better Gaussian Mechanism using Correlated Noise Christian Janos Lebeda 93 4 0 13 Aug 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 101 0 0 22 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 88 14 0 10 Jul 2024
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning Lynn Chua Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Daogao Liu Pasin Manurangsi Amer Sinha Chiyuan Zhang 104 14 0 20 Jun 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 124 10 0 27 May 2024
Closed-Form Bounds for DP-SGD against Record-level Inference Giovanni Cherubin Boris Köpf Andrew Paverd Shruti Tople Lukas Wutschitz Santiago Zanella Béguelin 89 2 0 22 Feb 2024
Privacy Profiles for Private Selection Antti Koskela Rachel Redberg Yu-Xiang Wang 86 2 0 09 Feb 2024
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation Ossi Raisa Hibiki Ito Antti Honkela 77 6 0 06 Feb 2024
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 75 3 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 218 26 0 09 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners Rachel Redberg Antti Koskela Yu-Xiang Wang 130 6 0 31 Dec 2023
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST) Xingyuan Zhao Fang Liu 55 0 0 20 Dec 2023
Privacy-Aware Document Visual Question Answering Rubèn Pérez Tito Khanh Nguyen Marlon Tobaben Raouf Kerkouche Mohamed Ali Souibgui ... Lei Kang Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas 70 13 0 15 Dec 2023
Analyzing the Shuffle Model through the Lens of Quantitative Information Flow Mireya Jurado Ramon G. Gonze Mário S. Alvim C. Palamidessi 61 1 0 22 May 2023
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 106 10 0 17 Apr 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 59 3 0 19 Feb 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova Ryan McKenna Zachary B. Charles Keith Rush Brendan McMahan 124 9 0 02 Feb 2023
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy Ergute Bao Yizheng Zhu X. Xiao Yifan Yang Beng Chin Ooi B. Tan Khin Mi Mi Aung FedML 82 19 0 08 Dec 2022
DPVIm: Differentially Private Variational Inference Improved Hibiki Ito Lukas Prediger Antti Honkela Samuel Kaski 78 3 0 28 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 136 54 0 02 Oct 2022
Individual Privacy Accounting with Gaussian Differential Privacy A. Koskela Marlon Tobaben Antti Honkela 107 20 0 30 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 65 7 0 20 Aug 2022
Faster Privacy Accounting via Evolving Discretization Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 104 15 0 10 Jul 2022
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions Vadym Doroshenko Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 79 43 0 10 Jul 2022
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime Wael Alghamdi S. Asoodeh Flavio du Pin Calmon O. Kosut Lalitha Sankar Fei Wei 38 10 0 25 Jun 2022
Bayesian Estimation of Differential Privacy Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople A. Salem Victor Rühle Andrew Paverd Mohammad Naseri Boris Köpf Daniel Jones 83 40 0 10 Jun 2022
Group privacy for personalized federated learning Filippo Galli Sayan Biswas Kangsoo Jung Tommaso Cucinotta C. Palamidessi FedML 77 12 0 07 Jun 2022
Tight Differential Privacy Guarantees for the Shuffle Model with $k$ -Randomized Response Sayan Biswas Kangsoo Jung C. Palamidessi 65 0 0 18 May 2022
Protecting Data from all Parties: Combining FHE and DP in Federated Learning Arnaud Grivet Sébert Renaud Sirdey Oana Stan Cédric Gouy-Pailler FedML 35 0 0 09 May 2022
Differential Private Discrete Noise Adding Mechanism: Conditions, Properties and Optimization Shuying Qin Jianping He Chongrong Fang J. Lam 41 6 0 19 Mar 2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams S. Denisov H. B. McMahan J. Rush Adam D. Smith Abhradeep Thakurta FedML 101 66 0 16 Feb 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 260 372 0 13 Oct 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu Wang 64 104 0 16 Jun 2021
Numerical Composition of Differential Privacy Sivakanth Gopi Y. Lee Lukas Wutschitz 97 183 0 05 Jun 2021
Tight Accounting in the Shuffle Model of Differential Privacy A. Koskela Mikko A. Heikkilä Antti Honkela FedML 65 17 0 01 Jun 2021
D3p -- A Python Package for Differentially-Private Probabilistic Programming Lukas Prediger Niki Loppi Samuel Kaski Antti Honkela 79 6 0 22 Mar 2021
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT A. Koskela Antti Honkela 69 20 0 24 Feb 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 109 42 0 05 Feb 2021