Peter Kairouz
35 papers · 2014–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (9) π Academic Marathon (11) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (7)
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Interdisciplinary Bridge
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Renaissance Researcher
(8)
πΊοΈ
Taxonomy Completionist
(30)
π€
Dynamic Duo
(10)
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Triple Crown
π±
Topic Pioneer
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Keyword Champion
π¬
Deep Specialist
(26)
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Century Club
(35)
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Trend Setter
β
The Questioner
β‘
Prolific Year
(7)
ποΈ
Keyword Collector
(98)
π₯
Unstoppable
(6)
Conferences
ICML (14)
NIPS (9)
AISTATS (3)
ACL (2)
COLT (2)
ICLR (2)
ALT (1)
EMNLP (1)
JMLR (1)
Top co-authors
Research topics
Keywords
differential privacy
(21)
federated learning
(13)
secure aggregation
(6)
stochastic gradient descent
(4)
communication efficiency
(4)
local differential privacy
(4)
communication constraint
(3)
randomized response
(3)
distribution estimation
(3)
large language model
(2)
staircase mechanism
(2)
distributed mean estimation
(2)
frequency estimation
(2)
privacy mechanism
(2)
privacy amplification
(2)
local privacy
(2)
distributed learning
(2)
communication compression
(2)
stochastic optimization
(2)
network analysis
(1)
Papers
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
ICML 2025
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
ICML 2024
Privacy-Preserving Instructions for Aligning Large Language Models
ICML 2024
One-shot Empirical Privacy Estimation for Federated Learning
ICLR 2024
Can LLMs get help from other LLMs without revealing private information?
ACL 2024
User Inference Attacks on Large Language Models
EMNLP 2024
Algorithms for bounding contribution for histogram estimation under user-level privacy
ICML 2023
Private Federated Learning with Autotuned Compression
ICML 2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
NIPS 2023
Unleashing the Power of Randomization in Auditing Differentially Private ML
NIPS 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
NIPS 2023
Federated Learning of Gboard Language Models with Differential Privacy
ACL 2023
Federated Heavy Hitter Recovery under Linear Sketching
ICML 2023
Optimal Compression of Locally Differentially Private Mechanisms
AISTATS 2022
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
ICML 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
ICML 2022
(Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces
COLT 2021
Pointwise Bounds for Distribution Estimation under Communication Constraints
NIPS 2021
The Skellam Mechanism for Differentially Private Federated Learning
NIPS 2021
Shuffled Model of Differential Privacy in Federated Learning
AISTATS 2021
Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints
ALT 2021
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints
COLT 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
ICML 2021
Practical and Private (Deep) Learning Without Sampling or Shuffling
ICML 2021
Generative Models for Effective ML on Private, Decentralized Datasets
ICLR 2020
Federated Heavy Hitters Discovery with Differential Privacy
AISTATS 2020
Privacy Amplification via Random Check-Ins
NIPS 2020
Breaking the Communication-Privacy-Accuracy Trilemma
NIPS 2020
Context Aware Local Differential Privacy
ICML 2020
Extremal Mechanisms for Local Differential Privacy
JMLR 2016
Metadata-conscious anonymous messaging
ICML 2016
Discrete Distribution Estimation under Local Privacy
ICML 2016
The Composition Theorem for Differential Privacy
ICML 2015
Secure Multi-party Differential Privacy
NIPS 2015
Extremal Mechanisms for Local Differential Privacy
NIPS 2014