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2012.12803
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Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
IEEE Annual Symposium on Foundations of Computer Science (FOCS), 2020
23 December 2020
Vitaly Feldman
Audra McMillan
Kunal Talwar
FedML
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Papers citing
"Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling"
50 / 110 papers shown
Mutual Information Bounds in the Shuffle Model
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Bayesian Advantage of Re-Identification Attack in the Shuffle Model
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332
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Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via
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246
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TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking
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Yunpeng Qi
JIazhao Zhang
Minghan Li
Shaoan Wang
...
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Zhibo Chen
Fangwei Zhong
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LRM
194
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08 Oct 2025
Piquant
ε
\varepsilon
ε
: Private Quantile Estimation in the Two-Server Model
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Jacob Imola
Fabrizio Boninsegna
Rasmus Pagh
A. Chowdhury
142
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17 Sep 2025
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Difei Xu
Meng Ding
Zihang Xiang
Jinhui Xu
Haiyan Zhao
244
2
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04 Sep 2025
Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Takao Murakami
Yuichi Sei
Reo Eriguchi
180
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02 Sep 2025
Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
Sayan Biswas
Philippe Chartier
Akash Dhasade
Tom Jurien
David Kerriou
Anne-Marie Kerrmarec
Mohammed Lemou
Franklin Tranie
M. Vos
Milos Vujasinovic
193
1
0
01 Sep 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
309
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0
10 Apr 2025
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
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Ayfer Özgür
FedML
416
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Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management
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Isha Mukherjee
265
3
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24 Feb 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities
International Conference on Learning Representations (ICLR), 2025
Tal Wagner
FedML
357
0
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21 Feb 2025
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
465
0
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31 Dec 2024
Differentially Private Multi-Sampling from Distributions
International Conference on Algorithmic Learning Theory (ALT), 2024
Albert Cheu
Debanuj Nayak
266
2
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Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma
Vishnu Vinod
Abhradeep Thakurta
Alekh Agarwal
Borja Balle
Christoph Dann
A. Raghuveer
OffRL
357
0
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18 Nov 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
Neural Information Processing Systems (NeurIPS), 2024
Lynn Chua
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
305
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DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
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Ujjwal Kumar
Antigoni Polychroniadou
FedML
512
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21 Oct 2024
Near Exact Privacy Amplification for Matrix Mechanisms
International Conference on Learning Representations (ICLR), 2024
Christopher A. Choquette-Choo
Arun Ganesh
Saminul Haque
Thomas Steinke
Abhradeep Thakurta
432
19
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08 Oct 2024
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem
Annual Review of Statistics and Its Application (ARSIA), 2024
Weijie J. Su
388
8
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14 Sep 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
AAAI Conference on Artificial Intelligence (AAAI), 2024
Puning Zhao
Yan Han
Zhe Liu
Chong Wang
Rongfei Fan
Qingming Li
231
1
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19 Aug 2024
Locally Private Histograms in All Privacy Regimes
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Clément L. Canonne
Abigail Gentle
494
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Private Collaborative Edge Inference via Over-the-Air Computation
Selim F. Yilmaz
Burak Hasircioglu
Li Qiao
Deniz Gunduz
FedML
422
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Enhanced Privacy Bound for Shuffle Model with Personalized Privacy
Yi-xiao Liu
Yuhan Liu
Li Xiong
Yujie Gu
Hong Chen
FedML
260
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25 Jul 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models
Jungang Yang
Zhe Ji
Liyao Xiang
330
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22 Jul 2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
Shaowei Wang
Changyu Dong
Xiangfu Song
Jin Li
Zhili Zhou
Di Wang
Han Wu
603
1
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26 Jun 2024
Universal Exact Compression of Differentially Private Mechanisms
Yanxiao Liu
Wei-Ning Chen
Ayfer Özgür
Cheuk Ting Li
263
13
0
28 May 2024
Improved Communication-Privacy Trade-offs in
L
2
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Wei-Ning Chen
Berivan Isik
Peter Kairouz
Albert No
Sewoong Oh
Zheng Xu
405
4
0
02 May 2024
How Private are DP-SGD Implementations?
Lynn Chua
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
459
23
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26 Mar 2024
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie
Zinan Lin
A. Backurs
Sivakanth Gopi
Da Yu
...
Haotian Jiang
Huishuai Zhang
Yin Tat Lee
Yue Liu
Sergey Yekhanin
SyDa
302
67
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04 Mar 2024
Differentially Private Decentralized Learning with Random Walks
International Conference on Machine Learning (ICML), 2024
Edwige Cyffers
A. Bellet
Jalaj Upadhyay
FedML
320
10
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12 Feb 2024
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
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Yang Cao
Yifei Ge
FedML
339
16
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DP-NMT: Scalable Differentially-Private Machine Translation
Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2023
Timour Igamberdiev
Doan Nam Long Vu
Felix Künnecke
Zhuo Yu
Jannik Holmer
Ivan Habernal
294
8
0
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User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Hilal Asi
Daogao Liu
282
15
0
07 Nov 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via
f
f
f
-Differential Privacy
Neural Information Processing Systems (NeurIPS), 2023
Chendi Wang
Buxin Su
Jiayuan Ye
Reza Shokri
Weijie J. Su
FedML
472
18
0
30 Oct 2023
Privacy Amplification for Matrix Mechanisms
International Conference on Learning Representations (ICLR), 2023
Christopher A. Choquette-Choo
Arun Ganesh
Thomas Steinke
Abhradeep Thakurta
400
18
0
24 Oct 2023
Differentially Private Aggregation via Imperfect Shuffling
International Test Conference (ITC), 2023
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Jelani Nelson
Samson Zhou
FedML
345
1
0
28 Aug 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
Conference on Computer and Communications Security (CCS), 2023
Kunal Talwar
Shan Wang
Audra McMillan
Vojta Jina
Vitaly Feldman
...
Congzheng Song
Karl Tarbe
Sebastian Vogt
L. Winstrom
Shundong Zhou
FedML
498
20
0
27 Jul 2023
Saibot: A Differentially Private Data Search Platform
Proceedings of the VLDB Endowment (PVLDB), 2023
Zezhou Huang
Jiaxiang Liu
Daniel Alabi
Raul Castro Fernandez
Eugene Wu
348
13
0
01 Jul 2023
Federated Linear Contextual Bandits with User-level Differential Privacy
International Conference on Machine Learning (ICML), 2023
Ruiquan Huang
Huanyu Zhang
Luca Melis
Milan Shen
Meisam Hajzinia
J. Yang
FedML
364
17
0
08 Jun 2023
Fast Optimal Locally Private Mean Estimation via Random Projections
Neural Information Processing Systems (NeurIPS), 2023
Hilal Asi
Vitaly Feldman
Jelani Nelson
Huy Le Nguyen
Kunal Talwar
FedML
286
15
0
07 Jun 2023
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
218
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
345
5
0
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Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice
USENIX Security Symposium (USENIX Security), 2023
Andrea Gadotti
Frederick Sell
Reethika Ramesh
Jinyuan Jia
232
28
0
14 Apr 2023
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model
AAAI Conference on Artificial Intelligence (AAAI), 2023
Yi-xiao Liu
Suyun Zhao
Li Xiong
Yuhan Liu
Hong Chen
FedML
227
18
0
11 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
Proceedings of the VLDB Endowment (PVLDB), 2023
Shaowei Wang
FedML
639
14
0
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Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Neural Information Processing Systems (NeurIPS), 2023
Wei-Ning Chen
Danni Song
Ayfer Özgür
Peter Kairouz
FedML
264
36
0
04 Apr 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Jinyan Su
Changhong Zhao
Haiyan Zhao
237
9
0
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Optimal and Private Learning from Human Response Data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Duc Nguyen
A. Zhang
239
2
0
10 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Journal of Artificial Intelligence Research (JAIR), 2023
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
597
263
0
01 Mar 2023
On Differentially Private Federated Linear Contextual Bandits
International Conference on Learning Representations (ICLR), 2023
Xingyu Zhou
Sayak Ray Chowdhury
FedML
472
16
0
27 Feb 2023
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