Peter Kairouz - RankMe

158 papers A* 31A 3B 5Misc 3Journal 103Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CLIOPATRA: Extracting Private Information from LLM Insights. CoRR Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, Peter Kairouz
2026	J	`jnl`	MAPLE: Metadata Augmented Private Language Evolution. CoRR Eli Chien, Yuzheng Hu, Ryan McKenna, Shanshan Wu, Zheng Xu, Peter Kairouz
2025	J	`jnl`	ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control. CoRR Yuzheng Hu, Ryan McKenna, Da Yu, Shanshan Wu, Han Zhao, Zheng Xu, Peter Kairouz
2025	J	`jnl`	Can Large Language Models Really Recognize Your Name? CoRR Dzung Pham, Peter Kairouz, Niloofar Mireshghallah, Eugene Bagdasarian, Chau Minh Pham, Amir Houmansadr
2025	J	`jnl`	How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy. CoRR Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
2025	J	`jnl`	Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? CoRR Marika Swanberg, Ryan McKenna, Edo Roth, Albert Cheu, Peter Kairouz
2025	A*	`conf`	Language Models May Verbatim Complete Text They Were Not Explicitly Trained On. ICML Ken Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot
2025	J	`jnl`	Language Models May Verbatim Complete Text They Were Not Explicitly Trained On. CoRR Ken Ziyu Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot
2025	J	`jnl`	Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data. CoRR Stefan Schoepf, Michael Curtis Mozer, Nicole Mitchell, Alexandra Brintrup, Georgios Kaissis, Peter Kairouz, Eleni Triantafillou
2025	J	`jnl`	Toward provably private analytics and insights into GenAI use. CoRR Albert Cheu, Artem Lagzdin, Brett McLarnon, Daniel Ramage, Katharine Daly, Marco Gruteser, Peter Kairouz, Rakshita Tandon, Stanislav Chiknavaryan, Timon Van Overveldt, Zoe Gong
2025	J	`jnl`	Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography. CoRR Ilia Shumailov, Daniel Ramage, Sarah Meiklejohn, Peter Kairouz, Florian Hartmann, Borja Balle, Eugene Bagdasarian
2025	J	`jnl`	Urania: Differentially Private Insights into AI Use. CoRR Daogao Liu, Edith Cohen, Badih Ghazi, Peter Kairouz, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Da Yu, Chiyuan Zhang
2024	J	`jnl`	Air Gap: Protecting Privacy-Conscious Conversational Agents. CoRR Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage
2024	A*	`conf`	AirGapAgent: Protecting Privacy-Conscious Conversational Agents. CCS Eugene Bagdasarian, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage
2024	J	`jnl`	Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR 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
2024	J	`jnl`	Can LLMs get help from other LLMs without revealing private information? CoRR Florian Hartmann, Duc-Hieu Tran, Peter Kairouz, Victor Carbune, Blaise Agüera y Arcas
2024	J	`jnl`	Confidential Federated Computations. CoRR 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
2024	J	`jnl`	Differentially Private Stream Processing at Scale. Proc. VLDB Endow. Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Eidan Cohen, Himani Apte, Jodi Spacek
2024	—	`conf`	Federated Learning in Practice: Reflections and Projections. TPS-ISA Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Federated Learning in Practice: Reflections and Projections. CoRR Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Improved Communication-Privacy Trade-offs in L CoRR Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
2024	A*	`conf`	Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy. ICML Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
2024	J	`jnl`	Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. CoRR A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna M. Wallach, Nicolas Papernot, Katherine Lee
2024	J	`jnl`	Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device User Data. CoRR Christopher Bian, Albert Cheu, Stanislav Chiknavaryan, Zoe Gong, Marco Gruteser, Oliver Guinan, Yannis Guzman, Peter Kairouz, Artem Lagzdin, Ryan McKenna, Grace Ni, Edo Roth, Maya Spivak, Timon Van Overveldt, Ren Yi
2024	A*	`conf`	One-shot Empirical Privacy Estimation for Federated Learning. ICLR Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar
2024	A*	`conf`	Privacy-Preserving Instructions for Aligning Large Language Models. ICML Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
2024	J	`jnl`	Privacy-Preserving Instructions for Aligning Large Language Models. CoRR Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
2024	J	`jnl`	Private federated discovery of out-of-vocabulary words for Gboard. CoRR Ziteng Sun, Peter Kairouz, Haicheng Sun, Adrià Gascón, Ananda Theertha Suresh
2024	J	`jnl`	Randomization Techniques to Mitigate the Risk of Copyright Infringement. CoRR Wei-Ning Chen, Peter Kairouz, Sewoong Oh, Zheng Xu
2024	J	`jnl`	Releasing Large-Scale Human Mobility Histograms with Differential Privacy. CoRR Christopher Bian, Albert Cheu, Yannis Guzman, Marco Gruteser, Peter Kairouz, Ryan McKenna, Edo Roth
2024	J	`jnl`	Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning. CoRR Marshall Ball, James Bell-Clark, Adrià Gascón, Peter Kairouz, Sewoong Oh, Zhiye Xie
2024	J	`jnl`	Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning. IACR Cryptol. ePrint Arch. Marshall Ball, James Bell-Clark, Adrià Gascón, Peter Kairouz, Sewoong Oh, Zhiye Xie
2024	A*	`conf`	User Inference Attacks on Large Language Models. EMNLP Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
2023	A*	`conf`	Algorithms for bounding contribution for histogram estimation under user-level privacy. ICML Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
2023	J	`jnl`	Breaking the Communication-Privacy-Accuracy Trilemma. IEEE Trans. Inf. Theory Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2023	J	`jnl`	Challenges towards the Next Frontier in Privacy. CoRR 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
2023	J	`jnl`	Differentially Private Stream Processing at Scale. CoRR Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek
2023	A*	`conf`	Federated Heavy Hitter Recovery under Linear Sketching. ICML Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
2023	J	`jnl`	Federated Heavy Hitter Recovery under Linear Sketching. CoRR Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
2023	—	`conf`	Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
2023	J	`jnl`	Federated Learning of Gboard Language Models with Differential Privacy. CoRR Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
2023	J	`jnl`	One-shot Empirical Privacy Estimation for Federated Learning. CoRR Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith M. Suriyakumar
2023	A*	`conf`	Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. NeurIPS Wei-Ning Chen, Dan Song, Ayfer Özgür, Peter Kairouz
2023	J	`jnl`	Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. CoRR Wei-Ning Chen, Dan Song, Ayfer Özgür, Peter Kairouz
2023	A*	`conf`	Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
2023	J	`jnl`	Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
2023	J	`jnl`	Private Federated Learning in Gboard. CoRR Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz
2023	A*	`conf`	Private Federated Learning with Autotuned Compression. ICML Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh
2023	J	`jnl`	Private Federated Learning with Autotuned Compression. CoRR Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh
2023	A*	`conf`	Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
2023	J	`jnl`	Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
2023	J	`jnl`	User Inference Attacks on Large Language Models. CoRR Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
2022	J	`jnl`	A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization. IEEE Trans. Inf. Theory Tyler Sypherd, Mario Díaz, John Kevin Cava, Gautam Dasarathy, Peter Kairouz, Lalitha Sankar
2022	A*	`conf`	Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. SP Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
2022	J	`jnl`	Federated learning and privacy. Commun. ACM Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
2022	J	`jnl`	Generating Fair Universal Representations Using Adversarial Models. IEEE Trans. Inf. Forensics Secur. Peter Kairouz, Jiachun Liao, Chong Huang, Maunil Vyas, Monica Welfert, Lalitha Sankar
2022	J	`jnl`	Histogram Estimation under User-level Privacy with Heterogeneous Data. CoRR Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
2022	A	`conf`	Optimal Compression of Locally Differentially Private Mechanisms. AISTATS Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis
2022	J	`jnl`	Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation. CoRR Jiang Zhang, Lillian Clark, Matthew A. Clark, Konstantinos Psounis, Peter Kairouz
2022	J	`jnl`	Privacy-utility trades in crowdsourced signal map obfuscation. Comput. Networks Jiang Zhang, Lillian Clark, Matthew A. Clark, Konstantinos Psounis, Peter Kairouz
2022	J	`jnl`	Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar Mohamed Hashim Elmirghani
2022	J	`jnl`	Series Editorial The Sixth Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani
2022	J	`jnl`	The Fifth Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani
2022	A*	`conf`	The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. ICML Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh
2022	J	`jnl`	The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. CoRR Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh
2022	A*	`conf`	The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. ICML Wei-Ning Chen, Ayfer Özgür, Peter Kairouz
2022	J	`jnl`	The Poisson binomial mechanism for secure and private federated learning. CoRR Wei-Ning Chen, Ayfer Özgür, Peter Kairouz
2022	J	`jnl`	Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. Proc. Priv. Enhancing Technol. Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista A. Bonawitz, Deborah Estrin, Marco Gruteser
2021	A*	`conf`	(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	J	`jnl`	Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
2021	A*	`conf`	Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints. COLT Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2021	J	`jnl`	Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints. CoRR Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2021	B	`conf`	Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints. ALT Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun
2021	J	`jnl`	Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
2021	J	`jnl`	Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation. CoRR Mario Díaz, Peter Kairouz, Lalitha Sankar
2021	B	`conf`	Neural Network-based Estimation of the MMSE. ISIT Mario Díaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar
2021	J	`jnl`	On the Renyi Differential Privacy of the Shuffle Model. CoRR Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
2021	A*	`conf`	On the Rényi Differential Privacy of the Shuffle Model. CCS Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
2021	J	`jnl`	Optimal Compression of Locally Differentially Private Mechanisms. CoRR Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis
2021	A*	`conf`	Pointwise Bounds for Distribution Estimation under Communication Constraints. NeurIPS Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2021	J	`jnl`	Pointwise Bounds for Distribution Estimation under Communication Constraints. CoRR Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2021	A*	`conf`	Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2021	J	`jnl`	Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2021	J	`jnl`	Privacy-first health research with federated learning. npj Digit. Medicine 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
2021	J	`jnl`	Series Editorial: Inauguration Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani
2021	J	`jnl`	Series Editorial: The Second Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar Mohamed Hashim Elmirghani
2021	J	`jnl`	Series Editorial: The Third Issue of the Series on Machine Learning in Communications and Networks. IEEE J. Sel. Areas Commun. Geoffrey Ye Li, Walid Saad, Ayfer Özgür, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gündüz, Jaafar M. H. Elmirghani
2021	A	`conf`	Shuffled Model of Differential Privacy in Federated Learning. AISTATS Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
2021	J	`jnl`	Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. IEEE J. Sel. Areas Inf. Theory Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
2021	A*	`conf`	The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. ICML Peter Kairouz, Ziyu Liu, Thomas Steinke
2021	J	`jnl`	The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. CoRR Peter Kairouz, Ziyu Liu, Thomas Steinke
2021	A*	`conf`	The Skellam Mechanism for Differentially Private Federated Learning. NeurIPS Naman Agarwal, Peter Kairouz, Ziyu Liu
2021	J	`jnl`	The Skellam Mechanism for Differentially Private Federated Learning. CoRR Naman Agarwal, Peter Kairouz, Ziyu Liu
2021	J	`jnl`	Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. CoRR Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista A. Bonawitz, Deborah Estrin, Marco Gruteser
2020	A*	`conf`	Breaking the Communication-Privacy-Accuracy Trilemma. NeurIPS Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2020	J	`jnl`	Breaking the Communication-Privacy-Accuracy Trilemma. CoRR Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
2020	A*	`conf`	Context Aware Local Differential Privacy. ICML Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
2020	J	`jnl`	DP-CGAN: Differentially Private Synthetic Data and Label Generation. CoRR Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten
2020	J	`jnl`	Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta
2020	A	`conf`	Federated Heavy Hitters Discovery with Differential Privacy. AISTATS Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li
2020	A*	`conf`	Generative Models for Effective ML on Private, Decentralized Datasets. ICLR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2020	A*	`conf`	Privacy Amplification via Random Check-Ins. NeurIPS Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta
2020	J	`jnl`	Privacy Amplification via Random Check-Ins. CoRR Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta
2020	J	`jnl`	Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. CoRR Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
2020	J	`jnl`	Sparse Combinatorial Group Testing. IEEE Trans. Inf. Theory Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
2019	B	`conf`	A Group Testing Approach to Random Access for Short-Packet Communication. ISIT Huseyin A. Inan, Surin Ahn, Peter Kairouz, Ayfer Özgür
2019	B	`conf`	A Tunable Loss Function for Binary Classification. ISIT Tyler Sypherd, Mario Díaz, Lalitha Sankar, Peter Kairouz
2019	J	`jnl`	A Tunable Loss Function for Binary Classification. CoRR Tyler Sypherd, Mario Díaz, Lalitha Sankar, Peter Kairouz
2019	J	`jnl`	A Tunable Loss Function for Classification. CoRR Tyler Sypherd, Mario Díaz, Harshit Laddha, Lalitha Sankar, Peter Kairouz, Gautam Dasarathy
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Can You Really Backdoor Federated Learning? CoRR Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan
2019	J	`jnl`	Context-Aware Local Differential Privacy. CoRR Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
2019	—	`conf`	DP-CGAN: Differentially Private Synthetic Data and Label Generation. CVPR Workshops Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten
2019	J	`jnl`	Federated Heavy Hitters Discovery with Differential Privacy. CoRR Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, Wei Li
2019	J	`jnl`	Generative Models for Effective ML on Private, Decentralized Datasets. CoRR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2019	J	`jnl`	Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints. CoRR Jiachun Liao, Chong Huang, Peter Kairouz, Lalitha Sankar
2019	J	`jnl`	On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. IEEE Trans. Inf. Theory Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür
2019	J	`jnl`	Theoretical Guarantees for Model Auditing with Finite Adversaries. CoRR Mario Díaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar
2018	B	`conf`	Energy-limited Massive Random Access via Noisy Group Testing. ISIT Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
2018	J	`jnl`	Generative Adversarial Privacy. CoRR Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
2018	Misc	`conf`	Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy. ACSSC Chong Huang, Peter Kairouz, Lalitha Sankar
2018	J	`jnl`	On the Contractivity of Privacy Mechanisms. CoRR Mario Díaz, Lalitha Sankar, Peter Kairouz
2018	—	`conf`	On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. Allerton Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür
2018	J	`jnl`	On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing. CoRR Huseyin A. Inan, Peter Kairouz, Mary Wootters, Ayfer Özgür
2018	—	`conf`	Siamese Generative Adversarial Privatizer for Biometric Data. ACCV (5) Witold Oleszkiewicz, Peter Kairouz, Karol J. Piczak, Ram Rajagopal, Tomasz Trzcinski
2018	J	`jnl`	Siamese Generative Adversarial Privatizer for Biometric Data. CoRR Witold Oleszkiewicz, Tomasz Wlodarczyk, Karol J. Piczak, Tomasz Trzcinski, Peter Kairouz, Ram Rajagopal
2018	—	`conf`	Understanding Compressive Adversarial Privacy. CDC Xiao Chen, Peter Kairouz, Ram Rajagopal
2018	J	`jnl`	Understanding Compressive Adversarial Privacy. CoRR Xiao Chen, Peter Kairouz, Ram Rajagopal
2017	—	`conf`	Asynchronous and noncoherent neighbor discovery for the IoT using sparse-graph codes. ICC Kabir Chandrasekher, Kangwook Lee, Peter Kairouz, Ramtin Pedarsani, Kannan Ramchandran
2017	J	`jnl`	Context-Aware Generative Adversarial Privacy. CoRR Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
2017	J	`jnl`	Context-Aware Generative Adversarial Privacy. Entropy Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
2017	J	`jnl`	Hiding the Rumor Source. IEEE Trans. Inf. Theory Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
2017	J	`jnl`	Sparse Combinatorial Group Testing for Low-Energy Massive Random Access. CoRR Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
2017	—	`conf`	Sparse group testing codes for low-energy massive random access. Allerton Huseyin A. Inan, Peter Kairouz, Ayfer Özgür
2017	J	`jnl`	The Composition Theorem for Differential Privacy. IEEE Trans. Inf. Theory Peter Kairouz, Sewoong Oh, Pramod Viswanath
2016	Misc	`conf`	Differentially private multi-party computation. CISS Peter Kairouz, Sewoong Oh, Pramod Viswanath
2016	A*	`conf`	Discrete Distribution Estimation under Local Privacy. ICML Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage
2016	J	`jnl`	Discrete Distribution Estimation under Local Privacy. CoRR Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage
2016	J	`jnl`	Extremal Mechanisms for Local Differential Privacy. J. Mach. Learn. Res. Peter Kairouz, Sewoong Oh, Pramod Viswanath
2016	J	`jnl`	Metadata-Conscious Anonymous Messaging. IEEE Trans. Signal Inf. Process. over Networks Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
2016	A*	`conf`	Metadata-conscious anonymous messaging. ICML Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
2016	A*	`conf`	Rumor Source Obfuscation on Irregular Trees. SIGMETRICS Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
2016	—	`—`	The fundamental limits of statistical data privacy Peter Kairouz
2015	J	`jnl`	Hiding the Rumor Source. CoRR Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
2015	—	`conf`	Secure Multi-party Differential Privacy. NIPS Peter Kairouz, Sewoong Oh, Pramod Viswanath
2015	A*	`conf`	Spy vs. Spy: Rumor Source Obfuscation. SIGMETRICS Giulia Fanti, Peter Kairouz, Sewoong Oh, Pramod Viswanath
2015	A*	`conf`	The Composition Theorem for Differential Privacy. ICML Peter Kairouz, Sewoong Oh, Pramod Viswanath
2015	J	`jnl`	The Staircase Mechanism in Differential Privacy. IEEE J. Sel. Top. Signal Process. Quan Geng, Peter Kairouz, Sewoong Oh, Pramod Viswanath
2014	—	`conf`	Extremal Mechanisms for Local Differential Privacy. NIPS Peter Kairouz, Sewoong Oh, Pramod Viswanath
2014	J	`jnl`	Extremal Mechanisms for Local Differential Privacy. CoRR Peter Kairouz, Sewoong Oh, Pramod Viswanath
2014	J	`jnl`	Optimality of Non-Interactive Randomized Response. CoRR Peter Kairouz, Sewoong Oh, Pramod Viswanath
2014	J	`jnl`	Spy vs. Spy: Rumor Source Obfuscation. CoRR Peter Kairouz, Sewoong Oh, Pramod Viswanath
2013	Misc	`conf`	Interference-aware rate control for bursty interference channels. ICASSP Peter Kairouz, Ahmed K. Sadek, Tamer Kadous
2013	J	`jnl`	MIMO Communications over Multi-Mode Optical Fibers: Capacity Analysis and Input-Output Coupling Schemes CoRR Peter Kairouz, Andrew C. Singer
2012	—	`conf`	A sphere decoding approach for the vector Viterbi algorithm. ACSCC Peter Kairouz, Aolin Xu, Naresh R. Shanbhag, Andrew C. Singer
2012	—	`conf`	Convergence rates for cooperation in heterogeneous populations. ACSCC Andrew J. Bean, Peter Kairouz, Andrew C. Singer