Distributed Differential Privacy via Shuffling

4 August 2018

Papers citing "Distributed Differential Privacy via Shuffling"

41 / 191 papers shown

Title
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images Ming Y. Lu Dehan Kong Jana Lipkova Richard J. Chen Rajendra Singh Drew F. K. Williamson Tiffany Y. Chen Faisal Mahmood FedML MedIm 28 167 0 21 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
FLAME: Differentially Private Federated Learning in the Shuffle Model Ruixuan Liu Yang Cao Hong Chen Ruoyang Guo Masatoshi Yoshikawa FedML 24 92 0 17 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
Strengthening Order Preserving Encryption with Differential Privacy Amrita Roy Chowdhury Bolin Ding S. Jha Weiran Liu Jingren Zhou 26 8 0 11 Sep 2020
Multi-Central Differential Privacy Thomas Steinke 8 8 0 11 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 26 25 0 17 Aug 2020
Local Differential Privacy and Its Applications: A Comprehensive Survey Mengmeng Yang Lingjuan Lyu Jun Zhao Tianqing Zhu Kwok-Yan Lam 13 137 0 09 Aug 2020
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy Lichao Sun Jianwei Qian Xun Chen FedML 6 205 0 31 Jul 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
BUDS: Balancing Utility and Differential Privacy by Shuffling Poushali Sengupta Sudipta Paul Subhankar Mishra FedML 9 6 0 07 Jun 2020
Continuous Release of Data Streams under both Centralized and Local Differential Privacy Tianhao Wang Joann Qiongna Chen Zhikun Zhang D. Su Yueqiang Cheng Zhou Li Ninghui Li S. Jha 20 73 0 24 May 2020
Privacy in Deep Learning: A Survey Fatemehsadat Mirshghallah Mohammadkazem Taram Praneeth Vepakomma Abhishek Singh Ramesh Raskar H. Esmaeilzadeh FedML 19 135 0 25 Apr 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 28 21 0 19 Apr 2020
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale Ryan M. Rogers S. Subramaniam Sean Peng D. Durfee Seunghyun Lee Santosh Kumar Kancha Shraddha Sahay P. Ahammad 27 77 0 14 Feb 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
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Shuang Song Kunal Talwar Abhradeep Thakurta 29 83 0 10 Jan 2020
The power of synergy in differential privacy: Combining a small curator with local randomizers A. Beimel Aleksandra Korolova Kobbi Nissim Or Sheffet Uri Stemmer 34 14 0 18 Dec 2019
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
Estimating Numerical Distributions under Local Differential Privacy Zitao Li Tianhao Wang Milan Lopuhaä-Zwakenberg B. Škorić Ninghui Li 16 85 0 02 Dec 2019
Separating Local & Shuffled Differential Privacy via Histograms Victor Balcer Albert Cheu FedML 44 67 0 15 Nov 2019
Pan-Private Uniformity Testing Kareem Amin Matthew Joseph Jieming Mao 16 23 0 04 Nov 2019
Context-Aware Local Differential Privacy Jayadev Acharya Kallista A. Bonawitz Peter Kairouz Daniel Ramage Ziteng Sun 4 36 0 31 Oct 2019
Linear and Range Counting under Metric-based Local Differential Privacy Zhuolun Xiang Bolin Ding Xi He Jingren Zhou 18 0 0 25 Sep 2019
Improved Summation from Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 48 22 0 24 Sep 2019
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 51 55 0 24 Sep 2019
Manipulation Attacks in Local Differential Privacy Albert Cheu Adam D. Smith Jonathan R. Ullman 23 93 0 20 Sep 2019
Improving Utility and Security of the Shuffler-based Differential Privacy Tianhao Wang Bolin Ding Min Xu Zhicong Huang Cheng Hong Jingren Zhou Ninghui Li S. Jha 17 11 0 30 Aug 2019
Exponential Separations in Local Differential Privacy Matthew Joseph Jieming Mao Aaron Roth 19 11 0 01 Jul 2019
Differentially Private Summation with Multi-Message Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 19 47 0 20 Jun 2019
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model Badih Ghazi Rasmus Pagh A. Velingker FedML 19 98 0 19 Jun 2019
Private Hypothesis Selection Mark Bun Gautam Kamath Thomas Steinke Zhiwei Steven Wu 14 89 0 30 May 2019
Privacy Amplification by Mixing and Diffusion Mechanisms Borja Balle Gilles Barthe Marco Gaboardi J. Geumlek 8 41 0 29 May 2019
The Role of Interactivity in Local Differential Privacy Matthew Joseph Jieming Mao Seth Neel Aaron Roth 33 65 0 07 Apr 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 42 236 0 07 Mar 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 8 16 0 29 Nov 2018
Locally Private Gaussian Estimation Matthew Joseph Janardhan Kulkarni Jieming Mao Zhiwei Steven Wu FedML 37 38 0 20 Nov 2018
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors Joshua Allen Bolin Ding Janardhan Kulkarni Harsha Nori O. Ohrimenko Sergey Yekhanin SyDa FedML 22 32 0 02 Jul 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