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Relaxed Marginal Consistency for Differentially Private Query Answering

Relaxed Marginal Consistency for Differentially Private Query Answering

13 September 2021
Ryan McKenna
Siddhant Pradhan
Daniel Sheldon
G. Miklau
ArXivPDFHTML

Papers citing "Relaxed Marginal Consistency for Differentially Private Query Answering"

9 / 9 papers shown
Title
NetDPSyn: Synthesizing Network Traces under Differential Privacy
NetDPSyn: Synthesizing Network Traces under Differential Privacy
Danyu Sun
Joann Qiongna Chen
Chen Gong
Tianhao Wang
Zhou Li
52
1
0
08 Sep 2024
Joint Selection: Adaptively Incorporating Public Information for Private
  Synthetic Data
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
Miguel Fuentes
Brett Mullins
Ryan McKenna
G. Miklau
Daniel Sheldon
35
4
0
12 Mar 2024
Decentralised, Scalable and Privacy-Preserving Synthetic Data Generation
Decentralised, Scalable and Privacy-Preserving Synthetic Data Generation
Vishal Ramesh
Rui Zhao
Naman Goel
26
1
0
30 Oct 2023
Utility Theory of Synthetic Data Generation
Utility Theory of Synthetic Data Generation
Shi Xu
W. Sun
Guang Cheng
25
5
0
17 May 2023
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under
  Convex Loss Functions
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions
Yingtai Xiao
Guanlin He
Danfeng Zhang
Daniel Kifer
29
4
0
14 May 2023
On the Utility Recovery Incapability of Neural Net-based Differential
  Private Tabular Training Data Synthesizer under Privacy Deregulation
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation
Yucong Liu
ChiHua Wang
Guang Cheng
29
7
0
28 Nov 2022
Private Synthetic Data for Multitask Learning and Marginal Queries
Private Synthetic Data for Multitask Learning and Marginal Queries
G. Vietri
Cédric Archambeau
Sergul Aydore
William Brown
Michael Kearns
Aaron Roth
Ankit Siva
Shuai Tang
Zhiwei Steven Wu
SyDa
28
29
0
15 Sep 2022
Archimedes Meets Privacy: On Privately Estimating Quantiles in High
  Dimensions Under Minimal Assumptions
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Omri Ben-Eliezer
Dan Mikulincer
Ilias Zadik
FedML
47
7
0
15 Aug 2022
Leveraging Public Data for Practical Private Query Release
Leveraging Public Data for Practical Private Query Release
Terrance Liu
G. Vietri
Thomas Steinke
Jonathan R. Ullman
Zhiwei Steven Wu
155
58
0
17 Feb 2021
1