Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead

8 June 2021

Papers citing "Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead"

37 / 37 papers shown

Title
$Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High $\varepsilon$ Regime$ Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High $\varepsilon$ Regime Charlie Harrison Pasin Manurangsi 26 0 0 07 Apr 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 53 0 0 21 Feb 2025
Differentially Private Empirical Cumulative Distribution Functions Antoine Barczewski Amal Mawass Jan Ramon FedML 52 0 0 10 Feb 2025
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 82 0 0 30 Nov 2024
Differential Privacy on Trust Graphs Badih Ghazi Ravi Kumar Pasin Manurangsi Serena Wang 23 1 0 15 Oct 2024
Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach Xiaoqing Wang Yuanjing Tian Keman Huang Bin Liang 34 1 0 21 Sep 2024
Locally Private Histograms in All Privacy Regimes Clément L. Canonne Abigail Gentle 33 1 0 09 Aug 2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling Shaowei Wang Changyu Dong Xiangfu Song Jin Li Zhili Zhou Di Wang Han Wu 41 0 0 26 Jun 2024
Differentially Private Multi-Site Treatment Effect Estimation Tatsuki Koga Kamalika Chaudhuri David Page OOD FedML CML 33 1 0 10 Oct 2023
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 31 2 0 28 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 26 9 0 11 Apr 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen Danni Song Ayfer Özgür Peter Kairouz FedML 28 25 0 04 Apr 2023
Multi-Message Shuffled Privacy in Federated Learning Antonious M. Girgis Suhas Diggavi FedML 28 8 0 22 Feb 2023
Does Federated Learning Really Need Backpropagation? H. Feng Tianyu Pang Chao Du Wei Chen Shuicheng Yan Min-Bin Lin FedML 36 10 0 28 Jan 2023
Anonymized Histograms in Intermediate Privacy Models Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi PICV 112 1 0 27 Oct 2022
Fine-grained Private Knowledge Distillation Yuntong Li Shaowei Wang Yingying Wang Jin Li Yuqiu Qian Bangzhou Xin Wei Yang 15 0 0 27 Jul 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Wei-Ning Chen Christopher A. Choquette-Choo Peter Kairouz A. Suresh FedML 34 63 0 07 Mar 2022
Shuffle Private Linear Contextual Bandits Sayak Ray Chowdhury Xingyu Zhou FedML 21 25 0 11 Feb 2022
Frequency Estimation in the Shuffle Model with Almost a Single Message Qiyao Luo Yilei Wang K. Yi FedML 35 11 0 12 Nov 2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation Eugene Bagdasaryan Peter Kairouz S. Mellem Adria Gascon Kallista A. Bonawitz D. Estrin Marco Gruteser 16 28 0 03 Nov 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 40 13 0 21 Oct 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 65 36 0 27 Sep 2021
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster C. Canonne Hongyi Lyu FedML 26 6 0 20 Aug 2021
Differential Privacy in the Shuffle Model: A Survey of Separations Albert Cheu FedML 38 39 0 25 Jul 2021
Locally Private k-Means in One Round Alisa Chang Badih Ghazi Ravi Kumar Pasin Manurangsi 39 30 0 20 Apr 2021
Differentially Private Histograms in the Shuffle Model from Fake Users Albert Cheu M. Zhilyaev FedML 37 27 0 06 Apr 2021
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming Ziyue Huang Yuan Qiu K. Yi Graham Cormode 16 25 0 05 Apr 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 35 232 0 12 Feb 2021
Privacy Enhancement via Dummy Points in the Shuffle Model Xiaochen Li Weiran Liu Hanwen Feng Kunzhe Huang Jinfei Liu K. Ren Zhan Qin FedML 20 5 0 29 Sep 2020
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 23 29 0 21 Sep 2020
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation Albert Cheu Jonathan R. Ullman FedML 25 21 0 17 Sep 2020
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs Antonious M. Girgis Deepesh Data Suhas Diggavi Peter Kairouz A. Suresh FedML 24 25 0 17 Aug 2020
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging C. Sabater A. Bellet J. Ramon FedML 21 18 0 12 Jun 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 74 6,079 0 10 Dec 2019
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 47 55 0 24 Sep 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 144 420 0 29 Nov 2018
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 91 278 0 02 Oct 2017