Kairouz, Peter

44 publications

ICLRW 2025 Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? Marika Swanberg, Ryan McKenna, Edo Roth, Albert Cheu, Peter Kairouz

ICML 2025 Language Models May Verbatim Complete Text They Were Not Explicitly Trained on Ken Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot

NeurIPS 2025 Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Eleni Triantafillou, Peter Kairouz, Nicole Elyse Mitchell, Niloofar Mireshghallah, Abigail Z. Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, Danah Boyd, Yejin Choi, Sanmi Koyejo, Fernando Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Miles Brundage, David Bau, Seth Neel, Hanna Wallach, Amy B. Cyphert, Mark Lemley, Nicolas Papernot, Katherine Lee

NeurIPSW 2024 Examining Data Compartmentalization for AI Governance Nicole Elyse Mitchell, Eleni Triantafillou, Peter Kairouz

ICML 2024 Improved Communication-Privacy Trade-Offs in $l_2$ Mean Estimation Under Streaming Differential Privacy Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu

ICLR 2024 One-Shot Empirical Privacy Estimation for Federated Learning Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar

ICML 2024 Privacy-Preserving Instructions for Aligning Large Language Models Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu

ICML 2023 Algorithms for Bounding Contribution for Histogram Estimation Under User-Level Privacy Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser

ICML 2023 Federated Heavy Hitter Recovery Under Linear Sketching Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

ICMLW 2023 Federated Heavy Hitter Recovery Under Linear Sketching Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

NeurIPSW 2023 One-Shot Empirical Privacy Estimation for Federated Learning Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh McMahan, Vinith Suriyakumar

NeurIPS 2023 Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-Off in Distributed Mean Estimation Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

ICMLW 2023 Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-Off in Distributed Mean Estimation Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

NeurIPS 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman

ICML 2023 Private Federated Learning with Autotuned Compression Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh

NeurIPS 2023 Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

ICMLW 2023 Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, Hugh Brendan McMahan, Alina Oprea, Sewoong Oh

NeurIPSW 2023 User Inference Attacks on LLMs Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher Choquette-Choo, Zheng Xu

NeurIPSW 2023 User Inference Attacks on Large Language Models Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

AISTATS 2022 Optimal Compression of Locally Differentially Private Mechanisms Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis

ICML 2022 The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Wei-Ning Chen, Christopher A Choquette Choo, Peter Kairouz, Ananda Theertha Suresh

ICML 2022 The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation Wei-Ning Chen, Ayfer Ozgur, Peter Kairouz

AISTATS 2021 Shuffled Model of Differential Privacy in Federated Learning Antonious Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, Ananda Theertha Suresh

COLT 2021 (Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces Peter Kairouz, Monica Ribero Diaz, Keith Rush, Abhradeep Thakurta

FnTML 2021 Advances and Open Problems in Federated Learning 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

COLT 2021 Breaking the Dimension Dependence in Sparse Distribution Estimation Under Communication Constraints Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

NeurIPSW 2021 Communication Efficient Federated Learning with Secure Aggregation and Differential Privacy Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz

ALT 2021 Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun

NeurIPS 2021 Pointwise Bounds for Distribution Estimation Under Communication Constraints Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

ICML 2021 Practical and Private (Deep) Learning Without Sampling or Shuffling Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

ICML 2021 The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz, Ziyu Liu, Thomas Steinke

NeurIPS 2021 The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal, Peter Kairouz, Ziyu Liu

NeurIPS 2020 Breaking the Communication-Privacy-Accuracy Trilemma Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

ICML 2020 Context Aware Local Differential Privacy Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

AISTATS 2020 Federated Heavy Hitters Discovery with Differential Privacy Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li

ICLR 2020 Generative Models for Effective ML on Private, Decentralized Datasets Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas

NeurIPS 2020 Privacy Amplification via Random Check-Ins Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, Abhradeep Guha Thakurta

CVPRW 2019 DP-CGAN: Differentially Private Synthetic Data and Label Generation Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten

ICML 2016 Discrete Distribution Estimation Under Local Privacy Peter Kairouz, Keith Bonawitz, Daniel Ramage

JMLR 2016 Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath

ICML 2016 Metadata-Conscious Anonymous Messaging Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath

NeurIPS 2015 Secure Multi-Party Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath

ICML 2015 The Composition Theorem for Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath

NeurIPS 2014 Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath