Private Aggregation from Fewer Anonymous Messages

24 September 2019

Papers citing "Private Aggregation from Fewer Anonymous Messages"

40 / 40 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
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 59 1 0 14 Mar 2025
Differential Privacy on Trust Graphs Badih Ghazi Ravi Kumar Pasin Manurangsi Serena Wang 25 1 0 15 Oct 2024
On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Adam Sealfon FedML 37 2 0 24 Jun 2024
FastLloyd: Federated, Accurate, Secure, and Tunable $k$ -Means Clustering with Differential Privacy Abdulrahman Diaa Thomas Humphries Florian Kerschbaum FedML 38 0 0 03 May 2024
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages Hilal Asi Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar Samson Zhou FedML 35 5 0 16 Apr 2024
Differentially Private Aggregation via Imperfect Shuffling Badih Ghazi Ravi Kumar Pasin Manurangsi Jelani Nelson Samson Zhou FedML 30 1 0 28 Aug 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 38 13 0 27 Jul 2023
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 39 2 0 28 May 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 40 2 0 18 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 26 9 0 11 Apr 2023
Private Federated Statistics in an Interactive Setting Audra McMillan O. Javidbakht Kunal Talwar Elliot Briggs Mike Chatzidakis ... Paul J. Pelzl Rehan Rishi Congzheng Song Shan Wang Shundong Zhou FedML 27 6 0 18 Nov 2022
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy Yi Liu Ke Sun Linglong Kong Bei Jiang 19 6 0 17 Oct 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 29 47 0 09 Aug 2022
Distributed Differential Privacy in Multi-Armed Bandits Sayak Ray Chowdhury Xingyu Zhou 30 12 0 12 Jun 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Wei-Ning Chen Christopher A. Choquette-Choo Peter Kairouz A. Suresh FedML 42 63 0 07 Mar 2022
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation Kunal Talwar FedML 35 10 0 22 Feb 2022
Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy Mary Scott Graham Cormode Carsten Maple 48 11 0 31 Jan 2022
Applying the Shuffle Model of Differential Privacy to Vector Aggregation Mary Scott Graham Cormode Carsten Maple FedML 18 3 0 10 Dec 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 24 28 0 03 Nov 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 42 13 0 21 Oct 2021
Infinitely Divisible Noise in the Low Privacy Regime Rasmus Pagh N. Stausholm FedML 33 2 0 13 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
Differential Privacy in the Shuffle Model: A Survey of Separations Albert Cheu FedML 41 40 0 25 Jul 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 32 48 0 08 Jun 2021
Locally Private k-Means in One Round Alisa Chang Badih Ghazi Ravi Kumar Pasin Manurangsi 39 30 0 20 Apr 2021
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model Kobbi Nissim Chao Yan 32 1 0 29 Mar 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 44 232 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 28 5 0 29 Sep 2020
On the Round Complexity of the Shuffle Model A. Beimel Iftach Haitner Kobbi Nissim Uri Stemmer FedML 15 15 0 28 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 30 21 0 17 Sep 2020
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications Sameer Wagh Xi He Ashwin Machanavajjhala Prateek Mittal 28 21 0 19 Apr 2020
Pure Differentially Private Summation from Anonymous Messages Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Rasmus Pagh A. Velingker FedML 31 46 0 05 Feb 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 76 6,091 0 10 Dec 2019
Improved Summation from Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 48 22 0 24 Sep 2019
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model Badih Ghazi Rasmus Pagh A. Velingker FedML 19 98 0 19 Jun 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 150 420 0 29 Nov 2018
The Power of The Hybrid Model for Mean Estimation Brendan Avent Yatharth Dubey Aleksandra Korolova 13 16 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