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2004.09481
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Connecting Robust Shuffle Privacy and Pan-Privacy
ACM-SIAM Symposium on Discrete Algorithms (SODA), 2020
20 April 2020
Victor Balcer
Albert Cheu
Matthew Joseph
Jieming Mao
FedML
Re-assign community
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Papers citing
"Connecting Robust Shuffle Privacy and Pan-Privacy"
32 / 32 papers shown
Uniformity Testing under User-Level Local Privacy
C. Canonne
Abigail Gentle
Vikrant Singhal
FedML
131
0
0
21 Oct 2025
How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations
Marc Damie
Florian Hahn
Andreas Peter
Jan Ramon
FedML
398
1
0
02 Jul 2025
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy
IEEE 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
International Conference on Learning Representations (ICLR), 2025
Tal Wagner
FedML
354
0
0
21 Feb 2025
Making Old Things New: A Unified Algorithm for Differentially Private Clustering
Max Dupré la Tour
Monika Henzinger
David Saulpic
FedML
237
5
0
17 Jun 2024
Analyzing the Shuffle Model through the Lens of Quantitative Information Flow
IEEE 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
Conference 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
Proceedings of the VLDB Endowment (PVLDB), 2023
Shaowei Wang
FedML
638
14
0
11 Apr 2023
Anonymized Histograms in Intermediate Privacy Models
Neural Information Processing Systems (NeurIPS), 2022
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
PICV
452
3
0
27 Oct 2022
Algorithms with More Granular Differential Privacy Guarantees
Information Technology Convergence and Services (ITCS), 2022
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Thomas Steinke
326
8
0
08 Sep 2022
Necessary Conditions in Multi-Server Differential Privacy
Information Technology Convergence and Services (ITCS), 2022
Albert Cheu
Chao Yan
208
9
0
17 Aug 2022
Tight Differential Privacy Guarantees for the Shuffle Model with
k
k
k
-Randomized Response
Foundations and Practice of Security (FPS), 2022
Sayan Biswas
Kangsoo Jung
C. Palamidessi
221
1
0
18 May 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
International Conference on Machine Learning (ICML), 2022
Wei-Ning Chen
Christopher A. Choquette-Choo
Peter Kairouz
A. Suresh
FedML
331
81
0
07 Mar 2022
Pure Differential Privacy from Secure Intermediaries
Albert Cheu
Chao Yan
FedML
336
10
0
19 Dec 2021
Frequency Estimation in the Shuffle Model with Almost a Single Message
Conference on Computer and Communications Security (CCS), 2021
Qiyao Luo
Yilei Wang
K. Yi
FedML
288
18
0
12 Nov 2021
Tight Bounds for Differentially Private Anonymized Histograms
SIAM Symposium on Simplicity in Algorithms (SSA), 2021
Pasin Manurangsi
PICV
219
6
0
05 Nov 2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
Proceedings on Privacy Enhancing Technologies (PoPETs), 2021
Eugene Bagdasaryan
Peter Kairouz
S. Mellem
Adria Gascon
Kallista A. Bonawitz
D. Estrin
Marco Gruteser
286
35
0
03 Nov 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
International Conference on Machine Learning (ICML), 2021
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Rasmus Pagh
Amer Sinha
FedML
379
42
0
27 Sep 2021
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster
C. Canonne
Hongyi Lyu
FedML
296
7
0
20 Aug 2021
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
Graham Cormode
I. Markov
FedML
206
11
0
03 Aug 2021
Differential Privacy in the Shuffle Model: A Survey of Separations
Albert Cheu
FedML
348
48
0
25 Jul 2021
Shuffle Private Stochastic Convex Optimization
International Conference on Learning Representations (ICLR), 2021
Albert Cheu
Matthew Joseph
Jieming Mao
Binghui Peng
FedML
316
29
0
17 Jun 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
International Conference on Machine Learning (ICML), 2020
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Rasmus Pagh
FedML
258
57
0
08 Jun 2021
Locally Private k-Means in One Round
International Conference on Machine Learning (ICML), 2021
Alisa Chang
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
325
41
0
20 Apr 2021
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model
Journal of Privacy and Confidentiality (JPC), 2021
Kobbi Nissim
Chao Yan
410
1
0
29 Mar 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
International Conference on Machine Learning (ICML), 2021
Peter Kairouz
Ziyu Liu
Thomas Steinke
FedML
529
285
0
12 Feb 2021
Inference under Information Constraints III: Local Privacy Constraints
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2021
Jayadev Acharya
C. Canonne
Cody R. Freitag
Ziteng Sun
Himanshu Tyagi
270
39
0
20 Jan 2021
Differentially Private Distributed Computation via Public-Private Communication Networks
Lei Wang
Yang Liu
I. Manchester
Guodong Shi
FedML
201
3
0
05 Jan 2021
Privacy Amplification by Decentralization
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Edwige Cyffers
A. Bellet
FedML
592
46
0
09 Dec 2020
On the Round Complexity of the Shuffle Model
A. Beimel
Iftach Haitner
Kobbi Nissim
Uri Stemmer
FedML
329
17
0
28 Sep 2020
On Distributed Differential Privacy and Counting Distinct Elements
Information Technology Convergence and Services (ITCS), 2020
Lijie Chen
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
FedML
241
33
0
21 Sep 2020
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation
Symposium on the Theory of Computing (STOC), 2020
Albert Cheu
Jonathan R. Ullman
FedML
559
24
0
17 Sep 2020
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