Peter Kairouz

Orcid: 0000-0001-6897-5937

According to our database1, Peter Kairouz authored at least 92 papers between 2012 and 2024.

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Bibliography

2024
Privacy-Preserving Instructions for Aligning Large Language Models.
CoRR, 2024

2023
Breaking the Communication-Privacy-Accuracy Trilemma.
IEEE Trans. Inf. Theory, February, 2023

User Inference Attacks on Large Language Models.
CoRR, 2023

Private Federated Learning in Gboard.
CoRR, 2023

Challenges towards the Next Frontier in Privacy.
CoRR, 2023

Differentially Private Stream Processing at Scale.
CoRR, 2023

One-shot Empirical Privacy Estimation for Federated Learning.
CoRR, 2023

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Unleashing the Power of Randomization in Auditing Differentially Private ML.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Private Federated Learning with Autotuned Compression.
Proceedings of the International Conference on Machine Learning, 2023

Algorithms for bounding contribution for histogram estimation under user-level privacy.
Proceedings of the International Conference on Machine Learning, 2023

Federated Heavy Hitter Recovery under Linear Sketching.
Proceedings of the International Conference on Machine Learning, 2023

Federated Learning of Gboard Language Models with Differential Privacy.
Proceedings of the The 61st Annual Meeting of the Association for Computational Linguistics: Industry Track, 2023

2022
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization.
IEEE Trans. Inf. Theory, 2022

Generating Fair Universal Representations Using Adversarial Models.
IEEE Trans. Inf. Forensics Secur., 2022

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation.
Proc. Priv. Enhancing Technol., 2022

Series Editorial The Sixth Issue of the Series on Machine Learning in Communications and Networks.
IEEE J. Sel. Areas Commun., 2022

Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks.
IEEE J. Sel. Areas Commun., 2022

The Fifth Issue of the Series on Machine Learning in Communications and Networks.
IEEE J. Sel. Areas Commun., 2022

The Poisson binomial mechanism for secure and private federated learning.
CoRR, 2022

Histogram Estimation under User-level Privacy with Heterogeneous Data.
CoRR, 2022

Privacy-utility trades in crowdsourced signal map obfuscation.
Comput. Networks, 2022

Federated learning and privacy.
Commun. ACM, 2022

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning.
Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation.
Proceedings of the International Conference on Machine Learning, 2022

The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning.
Proceedings of the International Conference on Machine Learning, 2022

Optimal Compression of Locally Differentially Private Mechanisms.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data.
ACM Queue, 2021

Privacy-first health research with federated learning.
npj Digit. Medicine, 2021

Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs.
IEEE J. Sel. Areas Inf. Theory, 2021

Series Editorial: The Third Issue of the Series on Machine Learning in Communications and Networks.
IEEE J. Sel. Areas Commun., 2021

Series Editorial: The Second Issue of the Series on Machine Learning in Communications and Networks.
IEEE J. Sel. Areas Commun., 2021

Series Editorial: Inauguration Issue of the Series on Machine Learning in Communications and Networks.
IEEE J. Sel. Areas Commun., 2021

Advances and Open Problems in Federated Learning.
Found. Trends Mach. Learn., 2021

Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation.
CoRR, 2021

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning.
CoRR, 2021

A Field Guide to Federated Optimization.
CoRR, 2021

Pointwise Bounds for Distribution Estimation under Communication Constraints.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Skellam Mechanism for Differentially Private Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Network-based Estimation of the MMSE.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Practical and Private (Deep) Learning Without Sampling or Shuffling.
Proceedings of the 38th International Conference on Machine Learning, 2021

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation.
Proceedings of the 38th International Conference on Machine Learning, 2021

(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces.
Proceedings of the Conference on Learning Theory, 2021

Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints.
Proceedings of the Conference on Learning Theory, 2021

On the Rényi Differential Privacy of the Shuffle Model.
Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints.
Proceedings of the Algorithmic Learning Theory, 2021

Shuffled Model of Differential Privacy in Federated Learning.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Sparse Combinatorial Group Testing.
IEEE Trans. Inf. Theory, 2020

Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs.
CoRR, 2020

Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning.
CoRR, 2020

Privacy Amplification via Random Check-Ins.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Context Aware Local Differential Privacy.
Proceedings of the 37th International Conference on Machine Learning, 2020

Generative Models for Effective ML on Private, Decentralized Datasets.
Proceedings of the 8th International Conference on Learning Representations, 2020

Federated Heavy Hitters Discovery with Differential Privacy.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing.
IEEE Trans. Inf. Theory, 2019

Advances and Open Problems in Federated Learning.
CoRR, 2019

Can You Really Backdoor Federated Learning?
CoRR, 2019

Theoretical Guarantees for Model Auditing with Finite Adversaries.
CoRR, 2019

Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints.
CoRR, 2019

A Tunable Loss Function for Classification.
CoRR, 2019

A Tunable Loss Function for Binary Classification.
Proceedings of the IEEE International Symposium on Information Theory, 2019

A Group Testing Approach to Random Access for Short-Packet Communication.
Proceedings of the IEEE International Symposium on Information Theory, 2019

DP-CGAN: Differentially Private Synthetic Data and Label Generation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018
Generative Adversarial Privacy.
CoRR, 2018

Siamese Generative Adversarial Privatizer for Biometric Data.
CoRR, 2018

On the Contractivity of Privacy Mechanisms.
CoRR, 2018

Energy-limited Massive Random Access via Noisy Group Testing.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Understanding Compressive Adversarial Privacy.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy.
Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

Siamese Generative Adversarial Privatizer for Biometric Data.
Proceedings of the Computer Vision - ACCV 2018, 2018

2017
The Composition Theorem for Differential Privacy.
IEEE Trans. Inf. Theory, 2017

Hiding the Rumor Source.
IEEE Trans. Inf. Theory, 2017

Context-Aware Generative Adversarial Privacy.
Entropy, 2017

Sparse Combinatorial Group Testing for Low-Energy Massive Random Access.
CoRR, 2017

Asynchronous and noncoherent neighbor discovery for the IoT using sparse-graph codes.
Proceedings of the IEEE International Conference on Communications, 2017

Sparse group testing codes for low-energy massive random access.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016
The fundamental limits of statistical data privacy
PhD thesis, 2016

Metadata-Conscious Anonymous Messaging.
IEEE Trans. Signal Inf. Process. over Networks, 2016

Extremal Mechanisms for Local Differential Privacy.
J. Mach. Learn. Res., 2016

Rumor Source Obfuscation on Irregular Trees.
Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science, 2016

Discrete Distribution Estimation under Local Privacy.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Differentially private multi-party computation.
Proceedings of the 2016 Annual Conference on Information Science and Systems, 2016

2015
The Staircase Mechanism in Differential Privacy.
IEEE J. Sel. Top. Signal Process., 2015

Spy vs. Spy: Rumor Source Obfuscation.
Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 2015

Secure Multi-party Differential Privacy.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Spy vs. Spy: Rumor Source Obfuscation.
CoRR, 2014

Optimality of Non-Interactive Randomized Response.
CoRR, 2014

2013
MIMO Communications over Multi-Mode Optical Fibers: Capacity Analysis and Input-Output Coupling Schemes
CoRR, 2013

Interference-aware rate control for bursty interference channels.
Proceedings of the IEEE International Conference on Acoustics, 2013

2012
A sphere decoding approach for the vector Viterbi algorithm.
Proceedings of the Conference Record of the Forty Sixth Asilomar Conference on Signals, 2012

Convergence rates for cooperation in heterogeneous populations.
Proceedings of the Conference Record of the Forty Sixth Asilomar Conference on Signals, 2012


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