The Privacy Blanket of the Shuffle Model

7 March 2019

Papers citing "The Privacy Blanket of the Shuffle Model"

42 / 142 papers shown

Title
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 25 232 0 12 Feb 2021
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 11 157 0 23 Dec 2020
Research Challenges in Designing Differentially Private Text Generation Mechanisms Oluwaseyi Feyisetan Abhinav Aggarwal Zekun Xu Nathanael Teissier 9 8 0 10 Dec 2020
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 42 39 0 09 Dec 2020
Toward Evaluating Re-identification Risks in the Local Privacy Model Takao Murakami Kenta Takahashi AAML 27 10 0 16 Oct 2020
Local Differential Privacy for Regret Minimization in Reinforcement Learning Evrard Garcelon Vianney Perchet Ciara Pike-Burke Matteo Pirotta 19 32 0 15 Oct 2020
Privacy Enhancement via Dummy Points in the Shuffle Model Xiaochen Li Weiran Liu Hanwen Feng Kunzhe Huang Jinfei Liu K. Ren Zhan Qin FedML 12 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 15 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 16 92 0 17 Sep 2020
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation Albert Cheu Jonathan R. Ullman FedML 17 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 16 25 0 17 Aug 2020
Breaking the Communication-Privacy-Accuracy Trilemma Wei-Ning Chen Peter Kairouz Ayfer Özgür 6 116 0 22 Jul 2020
Privacy Amplification via Random Check-Ins Borja Balle Peter Kairouz H. B. McMahan Om Thakkar Abhradeep Thakurta FedML 17 72 0 13 Jul 2020
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging C. Sabater A. Bellet J. Ramon FedML 13 18 0 12 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 12 73 0 24 May 2020
Privacy in Deep Learning: A Survey Fatemehsadat Mirshghallah Mohammadkazem Taram Praneeth Vepakomma Abhishek Singh Ramesh Raskar H. Esmaeilzadeh FedML 6 135 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 23 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 11 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 23 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 13 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 18 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 69 6,063 0 10 Dec 2019
Estimating Numerical Distributions under Local Differential Privacy Zitao Li Tianhao Wang Milan Lopuhaä-Zwakenberg B. Škorić Ninghui Li 11 85 0 02 Dec 2019
Separating Local & Shuffled Differential Privacy via Histograms Victor Balcer Albert Cheu FedML 32 67 0 15 Nov 2019
Improved Differentially Private Decentralized Source Separation for fMRI Data H. Imtiaz Jafar Mohammadi Rogers F. Silva Bradley T. Baker Sergey Plis Anand D. Sarwate Vince D. Calhoun OOD 16 5 0 28 Oct 2019
Linear and Range Counting under Metric-based Local Differential Privacy Zhuolun Xiang Bolin Ding Xi He Jingren Zhou 13 0 0 25 Sep 2019
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 39 55 0 24 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 12 11 0 30 Aug 2019
On the Power of Multiple Anonymous Messages Badih Ghazi Noah Golowich Ravi Kumar Rasmus Pagh A. Velingker FedML 15 6 0 29 Aug 2019
Differentially Private Summation with Multi-Message Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 14 47 0 20 Jun 2019
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model Badih Ghazi Rasmus Pagh A. Velingker FedML 11 98 0 19 Jun 2019
Privacy Amplification by Mixing and Diffusion Mechanisms Borja Balle Gilles Barthe Marco Gaboardi J. Geumlek 6 41 0 29 May 2019
Differential privacy with partial knowledge Damien Desfontaines Esfandiar Mohammadi Elisabeth Krahmer David Basin 17 10 0 02 May 2019
The Role of Interactivity in Local Differential Privacy Matthew Joseph Jieming Mao Seth Neel Aaron Roth 28 64 0 07 Apr 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
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 138 420 0 29 Nov 2018
The Power of The Hybrid Model for Mean Estimation Brendan Avent Yatharth Dubey Aleksandra Korolova 6 16 0 29 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 14 32 0 02 Jul 2018
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information John C. Duchi Feng Ruan 13 50 0 14 Jun 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 85 278 0 02 Oct 2017