Peter Kairouz

ORCID

Publication Activity (10 Years)

Years Active: 2012-2023
Publications (10 Years): 116

Top Venues

CoRR

ICML

NeurIPS

IEEE Trans. Inf. Theory

Publications

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 (2023)
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith Suriyakumar
One-shot Empirical Privacy Estimation for Federated Learning. CoRR (2023)
Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz
Private Federated Learning in Gboard. CoRR (2023)
Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
Federated Heavy Hitter Recovery under Linear Sketching. CoRR (2023)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Breaking the Communication-Privacy-Accuracy Trilemma. IEEE Trans. Inf. Theory 69 (2) (2023)
Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR (2023)
Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR (2023)
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 (2023)
Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
Federated Heavy Hitter Recovery under Linear Sketching. ICML (2023)
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)
Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh
Private Federated Learning with Autotuned Compression. ICML (2023)
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)
Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS (2023)
Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
User Inference Attacks on Large Language Models. CoRR (2023)
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
Algorithms for bounding contribution for histogram estimation under user-level privacy. ICML (2023)
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)
Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh
Private Federated Learning with Autotuned Compression. CoRR (2023)
Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek
Differentially Private Stream Processing at Scale. CoRR (2023)
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 (2023)
Wei-Ning Chen, Ayfer Özgür, Peter Kairouz
The Poisson binomial mechanism for secure and private federated learning. CoRR (2022)
Jiang Zhang, Lillian Clark, Matthew A. Clark, Konstantinos Psounis, Peter Kairouz
Privacy-utility trades in crowdsourced signal map obfuscation. Comput. Networks 215 (2022)
Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis
Optimal Compression of Locally Differentially Private Mechanisms. AISTATS (2022)
Jiang Zhang, Lillian Clark, Matthew A. Clark, Konstantinos Psounis, Peter Kairouz
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation. CoRR (2022)
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) (2022)
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) (2022)
Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. CoRR (2022)
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) (2022)
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) (2022)
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) (2022)
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. IEEE Symposium on Security and Privacy (2022)
Wei-Ning Chen, Ayfer Özgür, Peter Kairouz
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. ICML (2022)
Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
Federated learning and privacy. Commun. ACM 65 (4) (2022)
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
Histogram Estimation under User-level Privacy with Heterogeneous Data. CoRR (2022)
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)
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 (2022)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR (2021)
Peter Kairouz, Ziyu Liu, Thomas Steinke
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. ICML (2021)
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 Horvath, 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 (2021)
Peter Kairouz, Ziyu Liu, Thomas Steinke
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. CoRR (2021)
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) (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
On the Renyi Differential Privacy of the Shuffle Model. CoRR (2021)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Pointwise Bounds for Distribution Estimation under Communication Constraints. CoRR (2021)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints. COLT (2021)
Mario Díaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar
Neural Network-based Estimation of the MMSE. ISIT (2021)
Naman Agarwal, Peter Kairouz, Ziyu Liu
The Skellam Mechanism for Differentially Private Federated Learning. NeurIPS (2021)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints. CoRR (2021)
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) (2021)
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)
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 (2021)
Mario Díaz, Peter Kairouz, Lalitha Sankar
Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation. CoRR (2021)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML (2021)
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) (2021)
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) (2021)
Naman Agarwal, Peter Kairouz, Ziyu Liu
The Skellam Mechanism for Differentially Private Federated Learning. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Differential Privacy in Federated Learning. AISTATS (2021)
Abhin Shah, Wei-Ning Chen, Johannes Balle, Peter Kairouz, Lucas Theis
Optimal Compression of Locally Differentially Private Mechanisms. CoRR (2021)
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) (2021)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Pointwise Bounds for Distribution Estimation under Communication Constraints. NeurIPS (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
On the Rényi Differential Privacy of the Shuffle Model. CCS (2021)
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR (2021)
Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT (2021)
Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun
Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints. ALT (2021)
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) (2021)
Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta
Privacy Amplification via Random Check-Ins. CoRR (2020)
Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
Context Aware Local Differential Privacy. ICML (2020)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Breaking the Communication-Privacy-Accuracy Trilemma. NeurIPS (2020)
Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
Sparse Combinatorial Group Testing. IEEE Trans. Inf. Theory 66 (5) (2020)
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Breaking the Communication-Privacy-Accuracy Trilemma. CoRR (2020)
Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR (2020)
Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta
Privacy Amplification via Random Check-Ins. NeurIPS (2020)
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 (2020)
Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li
Federated Heavy Hitters Discovery with Differential Privacy. AISTATS (2020)
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)
Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten
DP-CGAN: Differentially Private Synthetic Data and Label Generation. CoRR (2020)
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) (2019)
Tyler Sypherd, Mario Díaz, Lalitha Sankar, Peter Kairouz
A Tunable Loss Function for Binary Classification. ISIT (2019)
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan
Can You Really Backdoor Federated Learning? CoRR (2019)
Jiachun Liao, Chong Huang, Peter Kairouz, Lalitha Sankar
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints. CoRR (2019)
Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, Wei Li
Federated Heavy Hitters Discovery with Differential Privacy. CoRR (2019)
Mario Díaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar
Theoretical Guarantees for Model Auditing with Finite Adversaries. CoRR (2019)
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 (2019)
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 (2019)
Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten
DP-CGAN: Differentially Private Synthetic Data and Label Generation. CVPR Workshops (2019)
Huseyin A. Inan, Surin Ahn, Peter Kairouz, Ayfer Özgür
A Group Testing Approach to Random Access for Short-Packet Communication. ISIT (2019)
Tyler Sypherd, Mario Díaz, Harshit Laddha, Lalitha Sankar, Peter Kairouz, Gautam Dasarathy
A Tunable Loss Function for Classification. CoRR (2019)
Tyler Sypherd, Mario Díaz, Lalitha Sankar, Peter Kairouz
A Tunable Loss Function for Binary Classification. CoRR (2019)
Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
Context-Aware Local Differential Privacy. CoRR (2019)
Xiao Chen, Peter Kairouz, Ram Rajagopal
Understanding Compressive Adversarial Privacy. CDC (2018)
Mario Díaz, Lalitha Sankar, Peter Kairouz
On the Contractivity of Privacy Mechanisms. CoRR (2018)
Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. CoRR (2018)
Witold Oleszkiewicz, Peter Kairouz, Karol J. Piczak, Ram Rajagopal, Tomasz Trzcinski
Siamese Generative Adversarial Privatizer for Biometric Data. ACCV (5) (2018)
Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
Generative Adversarial Privacy. CoRR (2018)
Witold Oleszkiewicz, Tomasz Wlodarczyk, Karol J. Piczak, Tomasz Trzcinski, Peter Kairouz, Ram Rajagopal
Siamese Generative Adversarial Privatizer for Biometric Data. CoRR (2018)
Chong Huang, Peter Kairouz, Lalitha Sankar
Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy. ACSSC (2018)
Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. Allerton (2018)
Xiao Chen, Peter Kairouz, Ram Rajagopal
Understanding Compressive Adversarial Privacy. CoRR (2018)
Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
Energy-limited Massive Random Access via Noisy Group Testing. ISIT (2018)
Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
Sparse group testing codes for low-energy massive random access. Allerton (2017)
Kabir Chandrasekher, Kangwook Lee, Peter Kairouz, Ramtin Pedarsani, Kannan Ramchandran
Asynchronous and noncoherent neighbor discovery for the IoT using sparse-graph codes. ICC (2017)
Giulia C. Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
Hiding the Rumor Source. IEEE Trans. Inf. Theory 63 (10) (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Peter Kairouz