Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2106.00477
Cited By
v1
v2
v3 (latest)
Tight Accounting in the Shuffle Model of Differential Privacy
1 June 2021
A. Koskela
Mikko A. Heikkilä
Antti Honkela
FedML
Re-assign community
ArXiv (abs)
PDF
HTML
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
Tal Wagner
FedML
96
0
0
21 Feb 2025
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
Shaowei Wang
FedML
107
9
0
11 Apr 2023
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
Seng Pei Liew
Tsubasa Takahashi
FedML
86
2
0
20 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
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
k
k
-Randomized Response
Sayan Biswas
Kangsoo Jung
C. Palamidessi
72
0
0
18 May 2022
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
Vitaly Feldman
Audra McMillan
Kunal Talwar
FedML
104
163
0
23 Dec 2020
1