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2020 – today
- 2024
[j25]Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Eidan Cohen, Himani Apte, Jodi Spacek:
Differentially Private Stream Processing at Scale. Proc. VLDB Endow. 17(12): 4145-4158 (2024)- [c50]Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar:
One-shot Empirical Privacy Estimation for Federated Learning. ICLR 2024 - [c49]Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu:
Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy. ICML 2024 - [c48]Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu:
Privacy-Preserving Instructions for Aligning Large Language Models. ICML 2024 - [i62]Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu:
Privacy-Preserving Instructions for Aligning Large Language Models. CoRR abs/2402.13659 (2024) - [i61]Florian Hartmann, Duc-Hieu Tran, Peter Kairouz, Victor Carbune, Blaise Agüera y Arcas:
Can LLMs get help from other LLMs without revealing private information? CoRR abs/2404.01041 (2024) - [i60]Hubert Eichner, Daniel Ramage, Kallista A. Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adrià Gascón, Marco Gruteser, Brendan McMahan:
Confidential Federated Computations. CoRR abs/2404.10764 (2024) - [i59]Ziteng Sun, Peter Kairouz, Haicheng Sun, Adrià Gascón, Ananda Theertha Suresh:
Private federated discovery of out-of-vocabulary words for Gboard. CoRR abs/2404.11607 (2024) - [i58]Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu:
Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy. CoRR abs/2405.02341 (2024) - [i57]Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage:
Air Gap: Protecting Privacy-Conscious Conversational Agents. CoRR abs/2405.05175 (2024) - [i56]Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Júlio C. S. Jacques Júnior, Ioannis Mitliagkas, Jun Wan, Lisheng Sun-Hosoya, Sergio Escalera, Gintare Karolina Dziugaite, Peter Triantafillou, Isabelle Guyon:
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR abs/2406.09073 (2024) - [i55]Christopher Bian, Albert Cheu, Yannis Guzman, Marco Gruteser, Peter Kairouz, Ryan McKenna, Edo Roth:
Releasing Large-Scale Human Mobility Histograms with Differential Privacy. CoRR abs/2407.03496 (2024) - [i54]Wei-Ning Chen, Peter Kairouz, Sewoong Oh, Zheng Xu:
Randomization Techniques to Mitigate the Risk of Copyright Infringement. CoRR abs/2408.13278 (2024) - 2023
- [j24]Wei-Ning Chen
, Peter Kairouz, Ayfer Özgür:
Breaking the Communication-Privacy-Accuracy Trilemma. IEEE Trans. Inf. Theory 69(2): 1261-1281 (2023) - [c47]Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang:
Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) 2023: 629-639 - [c46]Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh:
Federated Heavy Hitter Recovery under Linear Sketching. ICML 2023: 10997-11012 - [c45]Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser:
Algorithms for bounding contribution for histogram estimation under user-level privacy. ICML 2023: 21969-21996 - [c44]Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh:
Private Federated Learning with Autotuned Compression. ICML 2023: 34668-34708 - [c43]Wei-Ning Chen, Dan Song, Ayfer Özgür, Peter Kairouz:
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. NeurIPS 2023 - [c42]Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh:
Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS 2023 - [c41]Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman:
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS 2023 - [i53]Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith M. Suriyakumar:
One-shot Empirical Privacy Estimation for Federated Learning. CoRR abs/2302.03098 (2023) - [i52]Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek:
Differentially Private Stream Processing at Scale. CoRR abs/2303.18086 (2023) - [i51]Wei-Ning Chen, Dan Song, Ayfer Özgür, Peter Kairouz:
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. CoRR abs/2304.01541 (2023) - [i50]Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang:
Challenges towards the Next Frontier in Privacy. CoRR abs/2304.06929 (2023) - [i49]Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh:
Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR abs/2305.18447 (2023) - [i48]Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang:
Federated Learning of Gboard Language Models with Differential Privacy. CoRR abs/2305.18465 (2023) - [i47]Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman:
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR abs/2306.09396 (2023) - [i46]Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz:
Private Federated Learning in Gboard. CoRR abs/2306.14793 (2023) - [i45]Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh:
Private Federated Learning with Autotuned Compression. CoRR abs/2307.10999 (2023) - [i44]Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh:
Federated Heavy Hitter Recovery under Linear Sketching. CoRR abs/2307.13347 (2023) - [i43]Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu:
User Inference Attacks on Large Language Models. CoRR abs/2310.09266 (2023) - 2022
- [j23]Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage:
Federated learning and privacy. Commun. ACM 65(4): 90-97 (2022) - [j22]Jiang Zhang, Lillian Clark, Matthew A. Clark, Konstantinos Psounis, Peter Kairouz:
Privacy-utility trades in crowdsourced signal map obfuscation. Comput. Networks 215: 109187 (2022) - [j21]Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar Mohamed Hashim Elmirghani:
Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. 40(1): 1-4 (2022) - [j20]Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani:
The Fifth Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. 40(8): 2251-2253 (2022) - [j19]Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani:
Series Editorial The Sixth Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. 40(9): 2507-2509 (2022) - [j18]Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista A. Bonawitz, Deborah Estrin, Marco Gruteser:
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. Proc. Priv. Enhancing Technol. 2022(4): 162-182 (2022) - [j17]Peter Kairouz, Jiachun Liao, Chong Huang, Maunil Vyas, Monica Welfert, Lalitha Sankar:
Generating Fair Universal Representations Using Adversarial Models. IEEE Trans. Inf. Forensics Secur. 17: 1970-1985 (2022) - [j16]Tyler Sypherd, Mario Díaz, John Kevin Cava, Gautam Dasarathy, Peter Kairouz, Lalitha Sankar:
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization. IEEE Trans. Inf. Theory 68(9): 6021-6051 (2022) - [c40]Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. AISTATS 2022: 7680-7723 - [c39]Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh:
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. ICML 2022: 3056-3089 - [c38]Wei-Ning Chen, Ayfer Özgür, Peter Kairouz:
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. ICML 2022: 3490-3506 - [c37]Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage:
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. SP 2022: 1354-1371 - [i42]Jiang Zhang, Lillian Clark, Matthew A. Clark, Konstantinos Psounis, Peter Kairouz:
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation. CoRR abs/2201.04782 (2022) - [i41]Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh:
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. CoRR abs/2203.03761 (2022) - [i40]Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser:
Histogram Estimation under User-level Privacy with Heterogeneous Data. CoRR abs/2206.03008 (2022) - [i39]Wei-Ning Chen, Ayfer Özgür, Peter Kairouz:
The Poisson binomial mechanism for secure and private federated learning. CoRR abs/2207.09916 (2022) - 2021
- [j15]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, 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:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [j14]Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani:
Series Editorial: Inauguration Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. 39(1): 1-3 (2021) - [j13]Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar Mohamed Hashim Elmirghani:
Series Editorial: The Second Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. 39(7): 1855-1857 (2021) - [j12]Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani:
Series Editorial: The Third Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. 39(8): 2267-2270 (2021) - [j11]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. IEEE J. Sel. Areas Inf. Theory 2(1): 464-478 (2021) - [j10]Adam Sadilek, Luyang Liu, Dung Nguyen, Methun Kamruzzaman, Stylianos Serghiou, Benjamin Rader, Alex Ingerman, Stefan Mellem, Peter Kairouz, Elaine O. Nsoesie, Jamie Macfarlane, Anil Vullikanti, Madhav V. Marathe, Paul Eastham, John S. Brownstein, Blaise Agüera y Arcas, Michael D. Howell, John Hernandez:
Privacy-first health research with federated learning. npj Digit. Medicine 4 (2021) - [j9]Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage:
Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue 19(5): 87-114 (2021) - [c36]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Differential Privacy in Federated Learning. AISTATS 2021: 2521-2529 - [c35]Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun:
Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints. ALT 2021: 79-98 - [c34]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz:
On the Rényi Differential Privacy of the Shuffle Model. CCS 2021: 2321-2341 - [c33]Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints. COLT 2021: 1028-1059 - [c32]Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta:
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT 2021: 2717-2746 - [c31]Peter Kairouz, Ziyu Liu, Thomas Steinke:
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. ICML 2021: 5201-5212 - [c30]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML 2021: 5213-5225 - [c29]Mario Díaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar:
Neural Network-based Estimation of the MMSE. ISIT 2021: 1023-1028 - [c28]Naman Agarwal, Peter Kairouz, Ziyu Liu:
The Skellam Mechanism for Differentially Private Federated Learning. NeurIPS 2021: 5052-5064 - [c27]Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Pointwise Bounds for Distribution Estimation under Communication Constraints. NeurIPS 2021: 24593-24603 - [i38]Peter Kairouz, Ziyu Liu, Thomas Steinke:
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. CoRR abs/2102.06387 (2021) - [i37]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR abs/2103.00039 (2021) - [i36]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz:
On the Renyi Differential Privacy of the Shuffle Model. CoRR abs/2105.05180 (2021) - [i35]Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints. CoRR abs/2106.08597 (2021) - [i34]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i33]Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage:
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR abs/2108.10241 (2021) - [i32]Mario Díaz, Peter Kairouz, Lalitha Sankar:
Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation. CoRR abs/2108.12851 (2021) - [i31]Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Pointwise Bounds for Distribution Estimation under Communication Constraints. CoRR abs/2110.03189 (2021) - [i30]Naman Agarwal, Peter Kairouz, Ziyu Liu:
The Skellam Mechanism for Differentially Private Federated Learning. CoRR abs/2110.04995 (2021) - [i29]Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. CoRR abs/2111.00092 (2021) - [i28]Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista A. Bonawitz, Deborah Estrin, Marco Gruteser:
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. CoRR abs/2111.02356 (2021) - 2020
- [j8]Huseyin A. Inan, Peter Kairouz, Ayfer Özgür:
Sparse Combinatorial Group Testing. IEEE Trans. Inf. Theory 66(5): 2729-2742 (2020) - [c26]Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li:
Federated Heavy Hitters Discovery with Differential Privacy. AISTATS 2020: 3837-3847 - [c25]Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas:
Generative Models for Effective ML on Private, Decentralized Datasets. ICLR 2020 - [c24]Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context Aware Local Differential Privacy. ICML 2020: 52-62 - [c23]Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. NeurIPS 2020 - [c22]Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Breaking the Communication-Privacy-Accuracy Trilemma. NeurIPS 2020 - [i27]Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten:
DP-CGAN: Differentially Private Synthetic Data and Label Generation. CoRR abs/2001.09700 (2020) - [i26]Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. CoRR abs/2007.06605 (2020) - [i25]Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Breaking the Communication-Privacy-Accuracy Trilemma. CoRR abs/2007.11707 (2020) - [i24]Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta:
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR abs/2008.06570 (2020) - [i23]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. CoRR abs/2008.07180 (2020)
2010 – 2019
- 2019
- [j7]Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür:
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. IEEE Trans. Inf. Theory 65(9): 5592-5603 (2019) - [c21]Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten:
DP-CGAN: Differentially Private Synthetic Data and Label Generation. CVPR Workshops 2019: 98-104 - [c20]Huseyin A. Inan, Surin Ahn, Peter Kairouz, Ayfer Özgür:
A Group Testing Approach to Random Access for Short-Packet Communication. ISIT 2019: 96-100 - [c19]Tyler Sypherd, Mario Díaz, Lalitha Sankar, Peter Kairouz:
A Tunable Loss Function for Binary Classification. ISIT 2019: 2479-2483 - [i22]Tyler Sypherd, Mario Díaz, Lalitha Sankar, Peter Kairouz:
A Tunable Loss Function for Binary Classification. CoRR abs/1902.04639 (2019) - [i21]Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, Wei Li:
Federated Heavy Hitters Discovery with Differential Privacy. CoRR abs/1902.08534 (2019) - [i20]Tyler Sypherd, Mario Díaz, Harshit Laddha, Lalitha Sankar, Peter Kairouz, Gautam Dasarathy:
A Tunable Loss Function for Classification. CoRR abs/1906.02314 (2019) - [i19]Jiachun Liao, Chong Huang, Peter Kairouz, Lalitha Sankar:
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints. CoRR abs/1910.00411 (2019) - [i18]Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context-Aware Local Differential Privacy. CoRR abs/1911.00038 (2019) - [i17]Mario Díaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar:
Theoretical Guarantees for Model Auditing with Finite Adversaries. CoRR abs/1911.03405 (2019) - [i16]Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas:
Generative Models for Effective ML on Private, Decentralized Datasets. CoRR abs/1911.06679 (2019) - [i15]Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan:
Can You Really Backdoor Federated Learning? CoRR abs/1911.07963 (2019) - [i14]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. 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, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, 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:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [c18]Witold Oleszkiewicz, Peter Kairouz, Karol J. Piczak, Ram Rajagopal, Tomasz Trzcinski:
Siamese Generative Adversarial Privatizer for Biometric Data. ACCV (5) 2018: 482-497 - [c17]Chong Huang, Peter Kairouz, Lalitha Sankar:
Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy. ACSSC 2018: 2162-2166 - [c16]Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür:
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. Allerton 2018: 188-195 - [c15]Xiao Chen, Peter Kairouz, Ram Rajagopal:
Understanding Compressive Adversarial Privacy. CDC 2018: 6824-6831 - [c14]Huseyin A. Inan, Peter Kairouz, Ayfer Özgür:
Energy-limited Massive Random Access via Noisy Group Testing. ISIT 2018: 1101-1105 - [i13]Mario Díaz, Lalitha Sankar, Peter Kairouz:
On the Contractivity of Privacy Mechanisms. CoRR abs/1801.06255 (2018) - [i12]Witold Oleszkiewicz, Tomasz Wlodarczyk, Karol J. Piczak, Tomasz Trzcinski, Peter Kairouz, Ram Rajagopal:
Siamese Generative Adversarial Privatizer for Biometric Data. CoRR abs/1804.08757 (2018) - [i11]Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal:
Generative Adversarial Privacy. CoRR abs/1807.05306 (2018) - [i10]Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür:
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. CoRR abs/1808.01457 (2018) - [i9]Xiao Chen, Peter Kairouz, Ram Rajagopal:
Understanding Compressive Adversarial Privacy. CoRR abs/1809.08911 (2018) - 2017
- [j6]Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal:
Context-Aware Generative Adversarial Privacy. Entropy 19(12): 656 (2017) - [j5]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
The Composition Theorem for Differential Privacy. IEEE Trans. Inf. Theory 63(6): 4037-4049 (2017) - [j4]Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Hiding the Rumor Source. IEEE Trans. Inf. Theory 63(10): 6679-6713 (2017) - [c13]Huseyin A. Inan, Peter Kairouz, Ayfer Özgür:
Sparse group testing codes for low-energy massive random access. Allerton 2017: 658-665 - [c12]Kabir Chandrasekher, Kangwook Lee, Peter Kairouz, Ramtin Pedarsani, Kannan Ramchandran:
Asynchronous and noncoherent neighbor discovery for the IoT using sparse-graph codes. ICC 2017: 1-6 - [i8]Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal:
Context-Aware Generative Adversarial Privacy. CoRR abs/1710.09549 (2017) - [i7]Huseyin A. Inan, Peter Kairouz, Ayfer Özgür:
Sparse Combinatorial Group Testing for Low-Energy Massive Random Access. CoRR abs/1711.05403 (2017) - 2016
- [b1]Peter Kairouz:
The fundamental limits of statistical data privacy. University of Illinois Urbana-Champaign, USA, 2016 - [j3]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Extremal Mechanisms for Local Differential Privacy. J. Mach. Learn. Res. 17: 17:1-17:51 (2016) - [j2]Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Metadata-Conscious Anonymous Messaging. IEEE Trans. Signal Inf. Process. over Networks 2(4): 582-594 (2016) - [c11]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Differentially private multi-party computation. CISS 2016: 128-132 - [c10]Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Metadata-conscious anonymous messaging. ICML 2016: 108-116 - [c9]Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. ICML 2016: 2436-2444 - [c8]Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Rumor Source Obfuscation on Irregular Trees. SIGMETRICS 2016: 153-164 - [i6]Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. CoRR abs/1602.07387 (2016) - 2015
- [j1]Quan Geng, Peter Kairouz, Sewoong Oh, Pramod Viswanath:
The Staircase Mechanism in Differential Privacy. IEEE J. Sel. Top. Signal Process. 9(7): 1176-1184 (2015) - [c7]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
The Composition Theorem for Differential Privacy. ICML 2015: 1376-1385 - [c6]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Secure Multi-party Differential Privacy. NIPS 2015: 2008-2016 - [c5]Giulia Fanti, Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Spy vs. Spy: Rumor Source Obfuscation. SIGMETRICS 2015: 271-284 - [i5]Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Hiding the Rumor Source. CoRR abs/1509.02849 (2015) - 2014
- [c4]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Extremal Mechanisms for Local Differential Privacy. NIPS 2014: 2879-2887 - [i4]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Extremal Mechanisms for Local Differential Privacy. CoRR abs/1407.1338 (2014) - [i3]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Optimality of Non-Interactive Randomized Response. CoRR abs/1407.1546 (2014) - [i2]Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Spy vs. Spy: Rumor Source Obfuscation. CoRR abs/1412.8439 (2014) - 2013
- [c3]Peter Kairouz, Ahmed K. Sadek, Tamer Kadous:
Interference-aware rate control for bursty interference channels. ICASSP 2013: 4854-4858 - [i1]Peter Kairouz, Andrew C. Singer:
MIMO Communications over Multi-Mode Optical Fibers: Capacity Analysis and Input-Output Coupling Schemes. CoRR abs/1304.0422 (2013) - 2012
- [c2]Peter Kairouz, Aolin Xu, Naresh R. Shanbhag, Andrew C. Singer:
A sphere decoding approach for the vector Viterbi algorithm. ACSCC 2012: 114-118 - [c1]Andrew J. Bean, Peter Kairouz, Andrew C. Singer:
Convergence rates for cooperation in heterogeneous populations. ACSCC 2012: 531-534
Coauthor Index
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