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Differentially Private Histograms in the Shuffle Model from Fake Users
v1v2v3v4 (latest)

Differentially Private Histograms in the Shuffle Model from Fake Users

IEEE Symposium on Security and Privacy (IEEE S&P), 2021
6 April 2021
Albert Cheu
M. Zhilyaev
    FedML
ArXiv (abs)PDFHTML

Papers citing "Differentially Private Histograms in the Shuffle Model from Fake Users"

20 / 20 papers shown
Mutual Information Bounds in the Shuffle Model
Mutual Information Bounds in the Shuffle Model
Pengcheng Su
Haibo Cheng
Ping Wang
FedML
397
0
0
19 Nov 2025
PEEL: A Poisoning-Exposing Encoding Theoretical Framework for Local Differential Privacy
PEEL: A Poisoning-Exposing Encoding Theoretical Framework for Local Differential Privacy
Lisha Shuai
Jiuling Dong
Nan Zhang
Shaofeng Tan
Haokun Zhang
Zilong Song
Gaoya Dong
Xiaolong Yang
AAML
128
0
0
30 Oct 2025
Network-Aware Differential Privacy
Network-Aware Differential Privacy
Zhou Li
Yu Zheng
Tianhao Wang
Sang-Woo Jun
184
0
0
04 Sep 2025
Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Takao Murakami
Yuichi Sei
Reo Eriguchi
176
0
0
02 Sep 2025
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential PrivacyIEEE Symposium on Security and Privacy (S&P), 2025
Takao Murakami
Yuichi Sei
Reo Eriguchi
301
5
0
10 Apr 2025
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
On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning Attacks
On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning AttacksNetwork and Distributed System Security Symposium (NDSS), 2024
Xiaoguang Li
Zitao Li
Ninghui Li
Wenhai Sun
AAML
656
9
0
28 Jan 2025
Segmented Private Data Aggregation in the Multi-message Shuffle Model
Segmented Private Data Aggregation in the Multi-message Shuffle Model
Shaowei Wang
Hongqiao Chen
Sufen Zeng
Ruilin Yang
Hui Jiang
...
Kaiqi Yu
Rundong Mei
Shaozheng Huang
Wei Yang
Bangzhou Xin
FedML
464
0
0
31 Dec 2024
Locally Private Histograms in All Privacy Regimes
Locally Private Histograms in All Privacy RegimesInformation Technology Convergence and Services (ITCS), 2024
Clément L. Canonne
Abigail Gentle
482
3
0
09 Aug 2024
Analyzing the Shuffle Model through the Lens of Quantitative Information
  Flow
Analyzing the Shuffle Model through the Lens of Quantitative Information FlowIEEE Computer Security Foundations Symposium (CSF), 2023
Mireya Jurado
Ramon G. Gonze
Mário S. Alvim
C. Palamidessi
215
5
0
22 May 2023
Amplification by Shuffling without Shuffling
Amplification by Shuffling without ShufflingConference on Computer and Communications Security (CCS), 2023
Borja Balle
James Bell
Adria Gascon
FedML
344
5
0
18 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
Privacy Amplification via Shuffling: Unified, Simplified, and TightenedProceedings of the VLDB Endowment (PVLDB), 2023
Shaowei Wang
FedML
638
14
0
11 Apr 2023
Robustness of Locally Differentially Private Graph Analysis Against Poisoning
Robustness of Locally Differentially Private Graph Analysis Against PoisoningACM Asia Conference on Computer and Communications Security (AsiaCCS), 2022
Jacob Imola
A. Chowdhury
Kamalika Chaudhuri
AAML
331
8
0
25 Oct 2022
Group privacy for personalized federated learning
Group privacy for personalized federated learningInternational Conference on Information Systems Security and Privacy (ICISSP), 2022
Filippo Galli
Sayan Biswas
Kangsoo Jung
Tommaso Cucinotta
C. Palamidessi
FedML
247
18
0
07 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 ResponseFoundations and Practice of Security (FPS), 2022
Sayan Biswas
Kangsoo Jung
C. Palamidessi
222
1
0
18 May 2022
Frequency Estimation in the Shuffle Model with Almost a Single Message
Frequency Estimation in the Shuffle Model with Almost a Single MessageConference on Computer and Communications Security (CCS), 2021
Qiyao Luo
Yilei Wang
K. Yi
FedML
294
18
0
12 Nov 2021
User-Level Private Learning via Correlated Sampling
User-Level Private Learning via Correlated Sampling
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
FedML
300
16
0
21 Oct 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central
  Accuracy in Almost a Single Message
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single MessageInternational Conference on Machine Learning (ICML), 2021
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Rasmus Pagh
Amer Sinha
FedML
388
42
0
27 Sep 2021
Differential Privacy in the Shuffle Model: A Survey of Separations
Differential Privacy in the Shuffle Model: A Survey of Separations
Albert Cheu
FedML
348
48
0
25 Jul 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with
  Vanishing Communication Overhead
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication OverheadInternational Conference on Machine Learning (ICML), 2020
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Rasmus Pagh
FedML
258
57
0
08 Jun 2021
1
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