Prochlo: Strong Privacy for Analytics in the Crowd

2 October 2017

Papers citing "Prochlo: Strong Privacy for Analytics in the Crowd"

47 / 47 papers shown

Title
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 30 13 0 27 Jul 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 32 2 0 18 May 2023
Triangle Counting with Local Edge Differential Privacy T. Eden Quanquan C. Liu Sofya Raskhodnikova Adam D. Smith 68 12 0 03 May 2023
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice Andrea Gadotti Frederick Sell Reethika Ramesh Jinyuan Jia 27 18 0 14 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 23 9 0 11 Apr 2023
(Private) Kernelized Bandits with Distributed Biased Feedback Fengjiao Li Xingyu Zhou Bo Ji 25 5 0 28 Jan 2023
Almost Tight Error Bounds on Differentially Private Continual Counting Monika Henzinger Jalaj Upadhyay Sarvagya Upadhyay FedML 11 37 0 09 Nov 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 56 49 0 02 Oct 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 18 47 0 09 Aug 2022
Optimal Algorithms for Mean Estimation under Local Differential Privacy Hilal Asi Vitaly Feldman Kunal Talwar 22 41 0 05 May 2022
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation Kunal Talwar FedML 23 10 0 22 Feb 2022
Distributed Differentially Private Ranking Aggregation Baobao Song Qiujun Lan Yang Li Gang Li FedML 8 3 0 07 Feb 2022
Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy Mary Scott Graham Cormode Carsten Maple 35 11 0 31 Jan 2022
Pure Differential Privacy from Secure Intermediaries Albert Cheu Chao Yan FedML 12 9 0 19 Dec 2021
Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon Kamalika Chaudhuri Vianney Perchet Matteo Pirotta FedML 22 18 0 11 Dec 2021
Tight Bounds for Differentially Private Anonymized Histograms Pasin Manurangsi PICV 19 6 0 05 Nov 2021
An Uncertainty Principle is a Price of Privacy-Preserving Microdata John M. Abowd Robert Ashmead Ryan Cumings-Menon S. Garfinkel Daniel Kifer Philip Leclerc William Sexton Ashley Simpson Christine Task Pavel I Zhuravlev 16 16 0 25 Oct 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 35 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 59 36 0 27 Sep 2021
DPGen: Automated Program Synthesis for Differential Privacy Yuxin Wang Zeyu Ding Yingtai Xiao Daniel Kifer Danfeng Zhang SyDa 38 12 0 15 Sep 2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges S. Ulukus Salman Avestimehr Michael C. Gastpar S. Jafar Ravi Tandon Chao Tian FedML 20 64 0 30 Jul 2021
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates Zhuohang Li Luyang Liu Jiaxin Zhang Jian-Dong Liu FedML OOD AAML 25 10 0 03 Jul 2021
Shuffle Private Stochastic Convex Optimization Albert Cheu Matthew Joseph Jieming Mao Binghui Peng FedML 15 25 0 17 Jun 2021
On Large-Cohort Training for Federated Learning Zachary B. Charles Zachary Garrett Zhouyuan Huo Sergei Shmulyian Virginia Smith FedML 19 112 0 15 Jun 2021
Federated Learning with Buffered Asynchronous Aggregation John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Michael G. Rabbat Mani Malek Dzmitry Huba FedML 16 288 0 11 Jun 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 24 48 0 08 Jun 2021
Differentially Private Histograms in the Shuffle Model from Fake Users Albert Cheu M. Zhilyaev FedML 22 27 0 06 Apr 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 25 232 0 12 Feb 2021
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms Zeyu Ding Yuxin Wang Yingtai Xiao Guanhong Wang Danfeng Zhang Daniel Kifer 23 6 0 02 Dec 2020
Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics Rishabh Poddar Sukrit Kalra Avishay Yanai Ryan Deng Raluca A. Popa J. M. Hellerstein 25 68 0 26 Oct 2020
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 15 29 0 21 Sep 2020
Challenges of AI in Wireless Networks for IoT Ijaz Ahmad Shahriar Shahabuddin T. Kumar E. Harjula M. Meisel M. Juntti T. Sauter M. Ylianttila 8 18 0 09 Jul 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 34 113 0 11 Jun 2020
Connecting Robust Shuffle Privacy and Pan-Privacy Victor Balcer Albert Cheu Matthew Joseph Jieming Mao FedML 20 41 0 20 Apr 2020
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications Sameer Wagh Xi He Ashwin Machanavajjhala Prateek Mittal 23 21 0 19 Apr 2020
ARA : Aggregated RAPPOR and Analysis for Centralized Differential Privacy Sudipta Paul Subhankar Mishra 8 10 0 06 Jan 2020
Separating Local & Shuffled Differential Privacy via Histograms Victor Balcer Albert Cheu FedML 32 67 0 15 Nov 2019
Helen: Maliciously Secure Coopetitive Learning for Linear Models Wenting Zheng Raluca A. Popa Joseph E. Gonzalez Ion Stoica FedML 19 144 0 16 Jul 2019
Locally Differentially Private Data Collection and Analysis Teng Wang Jun Zhao Xinyu Yang Xuebin Ren 25 13 0 05 Jun 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 26 121 0 04 Jun 2019
The Role of Interactivity in Local Differential Privacy Matthew Joseph Jieming Mao Seth Neel Aaron Roth 28 64 0 07 Apr 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 28 236 0 07 Mar 2019
Federated Heavy Hitters Discovery with Differential Privacy Wennan Zhu Peter Kairouz H. B. McMahan Haicheng Sun Wei Li FedML 10 106 0 22 Feb 2019
Conclave: secure multi-party computation on big data (extended TR) Nikolaj Volgushev Malte Schwarzkopf Ben Getchell Mayank Varia A. Lapets Azer Bestavros 17 139 0 17 Feb 2019
Locally Private Gaussian Estimation Matthew Joseph Janardhan Kulkarni Jieming Mao Zhiwei Steven Wu FedML 34 38 0 20 Nov 2018
Fidelius: Protecting User Secrets from Compromised Browsers Saba Eskandarian Jonathan Cogan Sawyer Birnbaum Peh Chang Wei Brandon Dillon Franke ... Taresh K. Sethi Vishal Subbiah Michael Backes Giancarlo Pellegrino Dan Boneh 11 52 0 13 Sep 2018