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Tight Accounting in the Shuffle Model of Differential Privacy
v1v2v3 (latest)

Tight Accounting in the Shuffle Model of Differential Privacy

1 June 2021
A. Koskela
Mikko A. Heikkilä
Antti Honkela
    FedML
ArXiv (abs)PDFHTML

Papers citing "Tight Accounting in the Shuffle Model of Differential Privacy"

10 / 10 papers shown
Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities
Tal Wagner
FedML
96
0
0
21 Feb 2025
Analyzing the Shuffle Model through the Lens of Quantitative Information
  Flow
Analyzing the Shuffle Model through the Lens of Quantitative Information Flow
Mireya Jurado
Ramon G. Gonze
Mário S. Alvim
C. Palamidessi
77
1
0
22 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
Shaowei Wang
FedML
107
9
0
11 Apr 2023
Stronger Privacy Amplification by Shuffling for Rényi and Approximate
  Differential Privacy
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
Vitaly Feldman
Audra McMillan
Kunal Talwar
FedML
90
49
0
09 Aug 2022
Shuffle Gaussian Mechanism for Differential Privacy
Shuffle Gaussian Mechanism for Differential Privacy
Seng Pei Liew
Tsubasa Takahashi
FedML
86
2
0
20 Jun 2022
Group privacy for personalized federated learning
Group privacy for personalized federated learning
Filippo Galli
Sayan Biswas
Kangsoo Jung
Tommaso Cucinotta
C. Palamidessi
FedML
77
12
0
07 Jun 2022
Impact of Sampling on Locally Differentially Private Data Collection
Impact of Sampling on Locally Differentially Private Data Collection
Sayan Biswas
Graham Cormode
Carsten Maple
FedML
76
0
0
02 Jun 2022
Tight Differential Privacy Guarantees for the Shuffle Model with
  $k$-Randomized Response
Tight Differential Privacy Guarantees for the Shuffle Model with kkk-Randomized Response
Sayan Biswas
Kangsoo Jung
C. Palamidessi
72
0
0
18 May 2022
Unlocking High-Accuracy Differentially Private Image Classification
  through Scale
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Soham De
Leonard Berrada
Jamie Hayes
Samuel L. Smith
Borja Balle
97
233
0
28 Apr 2022
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy
  Amplification by Shuffling
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Vitaly Feldman
Audra McMillan
Kunal Talwar
FedML
104
163
0
23 Dec 2020
1