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Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs

Aug 17, 2020
Antonious M. Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, Ananda Theertha Suresh


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Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning

Aug 14, 2020
Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta


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Privacy Amplification via Random Check-Ins

Jul 30, 2020
Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta

* Updated proof for $(\epsilon_0, \delta_0)$-DP local randomizers 

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Breaking the Communication-Privacy-Accuracy Trilemma

Jul 22, 2020
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür

* 31 pages, 8 figures, submitted to NeurIPS 2020 

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DP-CGAN: Differentially Private Synthetic Data and Label Generation

Jan 27, 2020
Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten

* 7 pages, 4 figures 

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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao


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Can You Really Backdoor Federated Learning?

Dec 02, 2019
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan

* To appear at the 2nd International Workshop on Federated Learning for Data Privacy and Confidentiality at NeurIPS 2019 

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Generative Models for Effective ML on Private, Decentralized Datasets

Nov 15, 2019
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas

* 27 pages, 8 figures 

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Theoretical Guarantees for Model Auditing with Finite Adversaries

Nov 08, 2019
Mario Diaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar

* 18 pages, 1 figure 

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Context-Aware Local Differential Privacy

Oct 31, 2019
Jayadev Acharya, Keith Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun


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Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints

Sep 27, 2019
Jiachun Liao, Chong Huang, Peter Kairouz, Lalitha Sankar

* 28 pages, 11 Figures. arXiv admin note: text overlap with arXiv:1807.05306 

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A Tunable Loss Function for Classification

Jun 26, 2019
Tyler Sypherd, Mario Diaz, Harshit Laddha, Lalitha Sankar, Peter Kairouz, Gautam Dasarathy

* Corrected email address 

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A Tunable Loss Function for Binary Classification

Mar 19, 2019
Tyler Sypherd, Mario Diaz, Lalitha Sankar, Peter Kairouz

* 9 pages, 1 figure, ISIT 2019 

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Siamese Generative Adversarial Privatizer for Biometric Data

Oct 08, 2018
Witold Oleszkiewicz, Peter Kairouz, Karol Piczak, Ram Rajagopal, Tomasz Trzcinski

* Paper accepted to ACCV 2018 (Asian Conference on Computer Vision) 

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Understanding Compressive Adversarial Privacy

Oct 02, 2018
Xiao Chen, Peter Kairouz, Ram Rajagopal


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Generative Adversarial Privacy

Jul 13, 2018
Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal

* A preliminary version of this work was presented at the Privacy in Machine Learning and Artificial Intelligence Workshop, ICML 2018. arXiv admin note: text overlap with arXiv:1710.09549 

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Context-Aware Generative Adversarial Privacy

Dec 03, 2017
Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal

* Improved version of a paper accepted by Entropy Journal, Special Issue on Information Theory in Machine Learning and Data Science 

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Discrete Distribution Estimation under Local Privacy

Jun 15, 2016
Peter Kairouz, Keith Bonawitz, Daniel Ramage

* 23 pages, 12 figures, submitted to ICML 2016 (under review) 

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Spy vs. Spy: Rumor Source Obfuscation

Apr 26, 2015
Giulia Fanti, Peter Kairouz, Sewoong Oh, Pramod Viswanath

* 14 pages 10 figures 

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