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On the Power of Multiple Anonymous Messages
v1v2v3v4 (latest)

On the Power of Multiple Anonymous Messages

29 August 2019
Badih Ghazi
Noah Golowich
Ravi Kumar
Rasmus Pagh
A. Velingker
    FedML
ArXiv (abs)PDFHTML

Papers citing "On the Power of Multiple Anonymous Messages"

6 / 6 papers shown
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities
Learning from End User Data with Shuffled Differential Privacy over Kernel DensitiesInternational Conference on Learning Representations (ICLR), 2025
Tal Wagner
FedML
354
0
0
21 Feb 2025
Privacy Enhancement via Dummy Points in the Shuffle Model
Privacy Enhancement via Dummy Points in the Shuffle Model
Xiaochen Li
Weiran Liu
Hanwen Feng
Kunzhe Huang
Jinfei Liu
K. Ren
Zhan Qin
FedML
376
5
0
29 Sep 2020
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical
  Evaluation
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
Ulfar Erlingsson
Vitaly Feldman
Ilya Mironov
A. Raghunathan
Shuang Song
Kunal Talwar
Abhradeep Thakurta
311
92
0
10 Jan 2020
Separating Local & Shuffled Differential Privacy via Histograms
Separating Local & Shuffled Differential Privacy via HistogramsInternational Test Conference (ITC), 2019
Victor Balcer
Albert Cheu
FedML
472
76
0
15 Nov 2019
Private Aggregation from Fewer Anonymous Messages
Private Aggregation from Fewer Anonymous MessagesInternational Conference on the Theory and Application of Cryptographic Techniques (EUROCRYPT), 2019
Badih Ghazi
Pasin Manurangsi
Rasmus Pagh
A. Velingker
FedML
240
57
0
24 Sep 2019
Improving Utility and Security of the Shuffler-based Differential
  Privacy
Improving Utility and Security of the Shuffler-based Differential Privacy
Tianhao Wang
Bolin Ding
Min Xu
Zhicong Huang
Cheng Hong
Jingren Zhou
Ninghui Li
S. Jha
350
11
0
30 Aug 2019
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