The Power of Factorization Mechanisms in Local and Central Differential Privacy

19 November 2019

Papers citing "The Power of Factorization Mechanisms in Local and Central Differential Privacy"

19 / 19 papers shown

Title
Differential Privacy for Network Assortativity Fei Ma Jinzhi Ouyang Xincheng Hu 42 0 0 06 May 2025
Binned Group Algebra Factorization for Differentially Private Continual Counting Monika Henzinger Nikita P. Kalinin Jalaj Upadhyay 29 2 0 06 Apr 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 87 0 0 28 Jan 2025
Differentially Private Online Federated Learning with Correlated Noise Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson FedML 49 1 0 10 Jan 2025
On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Adam Sealfon FedML 37 2 0 24 Jun 2024
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity A. F. Pour Hassan Ashtiani S. Asoodeh 43 0 0 09 Dec 2023
Some Constructions of Private, Efficient, and Optimal $K$ -Norm and Elliptic Gaussian Noise Matthew Joseph Alexander Yu 29 2 0 27 Sep 2023
A Unifying Framework for Differentially Private Sums under Continual Observation Monika Henzinger Jalaj Upadhyay Sarvagya Upadhyay FedML 31 15 0 18 Jul 2023
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions Yingtai Xiao Guanlin He Danfeng Zhang Daniel Kifer 34 4 0 14 May 2023
PRIMO: Private Regression in Multiple Outcomes Seth Neel 23 0 0 07 Mar 2023
General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation Aleksandar Nikolov Haohua Tang 49 4 0 31 Jan 2023
Answering Private Linear Queries Adaptively using the Common Mechanism Yingtai Xiao Guanhong Wang Danfeng Zhang Daniel Kifer 65 7 0 30 Nov 2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning Christopher A. Choquette-Choo H. B. McMahan Keith Rush Abhradeep Thakurta 39 42 0 12 Nov 2022
Almost Tight Error Bounds on Differentially Private Continual Counting Monika Henzinger Jalaj Upadhyay Sarvagya Upadhyay FedML 21 38 0 09 Nov 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 33 60 0 16 Feb 2022
The Price of Differential Privacy under Continual Observation Palak Jain Sofya Raskhodnikova Satchit Sivakumar Adam D. Smith 31 51 0 01 Dec 2021
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods Terrance Liu G. Vietri Zhiwei Steven Wu SyDa 33 61 0 14 Jun 2021
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 23 29 0 21 Sep 2020
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 91 278 0 02 Oct 2017