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08/24/2023
Counting Distinct Elements Under Person-Level Differential Privacy
We study the problem of counting the number of distinct elements in a da...
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05/22/2023
Differentially Private Medians and Interior Points for Non-Pathological Data
We construct differentially private estimators with low sample complexit...
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05/22/2023
Faster Differentially Private Convex Optimization via Second-Order Methods
Differentially private (stochastic) gradient descent is the workhorse of...
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05/15/2023
Privacy Auditing with One (1) Training Run
We propose a scheme for auditing differentially private machine learning...
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03/31/2023
Differentially Private Stream Processing at Scale
We design, to the best of our knowledge, the first differentially privat...
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02/19/2023
Why Is Public Pretraining Necessary for Private Model Training?
In the privacy-utility tradeoff of a model trained on benchmark language...
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02/15/2023
Tight Auditing of Differentially Private Machine Learning
Auditing mechanisms for differential privacy use probabilistic means to ...
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01/30/2023
A Bias-Variance-Privacy Trilemma for Statistical Estimation
The canonical algorithm for differentially private mean estimation is to...
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10/02/2022
Composition of Differential Privacy Privacy Amplification by Subsampling
This chapter is meant to be part of the book "Differential Privacy for A...
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11/08/2021
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
We give the first polynomial-time, polynomial-sample, differentially pri...
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10/07/2021
Hyperparameter Tuning with Renyi Differential Privacy
For many differentially private algorithms, such as the prominent noisy ...
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06/17/2021
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes
We give a novel, unified derivation of conditional PAC-Bayesian and mutu...
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05/28/2021
Privately Learning Subspaces
Private data analysis suffers a costly curse of dimensionality. However,...
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05/15/2021
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise
The permute-and-flip mechanism is a recently proposed differentially pri...
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02/17/2021
Leveraging Public Data for Practical Private Query Release
In many statistical problems, incorporating priors can significantly imp...
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02/12/2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
We consider training models on private data that is distributed across u...
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09/11/2020
Multi-Central Differential Privacy
Differential privacy is typically studied in the central model where a t...
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07/10/2020
New Oracle-Efficient Algorithms for Private Synthetic Data Release
We present three new algorithms for constructing differentially private ...
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03/31/2020
The Discrete Gaussian for Differential Privacy
We show how to efficiently provide differentially private answers to cou...
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01/24/2020
Reasoning About Generalization via Conditional Mutual Information
We provide an information-theoretic framework for studying the generaliz...
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06/06/2019
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
The simplest and most widely applied method for guaranteeing differentia...
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05/30/2019
Private Hypothesis Selection
We provide a differentially private algorithm for hypothesis selection. ...
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12/07/2018
A Hybrid Approach to Privacy-Preserving Federated Learning
Training machine learning models often requires data from multiple parti...
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11/09/2018
Towards Instance-Optimal Private Query Release
We study efficient mechanisms for the query release problem in different...
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06/15/2018
The Limits of Post-Selection Generalization
While statistics and machine learning offers numerous methods for ensuri...
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12/19/2017
Calibrating Noise to Variance in Adaptive Data Analysis
Datasets are often used multiple times and each successive analysis may ...
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06/15/2017