ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2212.00936
  4. Cited By
Integer Subspace Differential Privacy

Integer Subspace Differential Privacy

2 December 2022
Prathamesh Dharangutte
Jie Gao
Ruobin Gong
Fang-Yi Yu
ArXivPDFHTML

Papers citing "Integer Subspace Differential Privacy"

11 / 11 papers shown
Title
Exact Privacy Guarantees for Markov Chain Implementations of the
  Exponential Mechanism with Artificial Atoms
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Jeremy Seeman
M. Reimherr
Aleksandra B. Slavkovic
67
11
0
03 Apr 2022
Subspace Differential Privacy
Subspace Differential Privacy
Jie Gao
Ruobin Gong
Fang-Yi Yu
20
17
0
26 Aug 2021
Faster Differentially Private Samplers via Rényi Divergence Analysis
  of Discretized Langevin MCMC
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Arun Ganesh
Kunal Talwar
FedML
34
40
0
27 Oct 2020
Bias and Variance of Post-processing in Differential Privacy
Bias and Variance of Post-processing in Differential Privacy
Keyu Zhu
Pascal Van Hentenryck
Ferdinando Fioretto
29
42
0
09 Oct 2020
Congenial Differential Privacy under Mandated Disclosure
Congenial Differential Privacy under Mandated Disclosure
Ruobin Gong
Xiangxu Meng
11
26
0
24 Aug 2020
SoK: Differential Privacies
SoK: Differential Privacies
Damien Desfontaines
Balázs Pejó
54
124
0
04 Jun 2019
Estimating Convergence of Markov chains with L-Lag Couplings
Estimating Convergence of Markov chains with L-Lag Couplings
N. Biswas
Pierre E. Jacob
Paul Vanetti
46
47
0
23 May 2019
Constrained Differential Privacy for Count Data
Constrained Differential Privacy for Count Data
Graham Cormode
Tejas D. Kulkarni
D. Srivastava
18
5
0
02 Oct 2017
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu Wang
S. Fienberg
Alex Smola
46
248
0
26 Feb 2015
Privacy and Statistical Risk: Formalisms and Minimax Bounds
Privacy and Statistical Risk: Formalisms and Minimax Bounds
Rina Foygel Barber
John C. Duchi
PILM
58
91
0
15 Dec 2014
Classical Hardness of Learning with Errors
Classical Hardness of Learning with Errors
Zvika Brakerski
Adeline Langlois
Chris Peikert
O. Regev
D. Stehlé
50
697
0
03 Jun 2013
1