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07/14/2023
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
Fingerprinting arguments, first introduced by Bun, Ullman, and Vadhan (S...
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06/01/2023
TMI! Finetuned Models Leak Private Information from their Pretraining Data
Transfer learning has become an increasingly popular technique in machin...
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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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02/03/2023
From Robustness to Privacy and Back
We study the relationship between two desiderata of algorithms in statis...
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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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09/07/2022
Multitask Learning via Shared Features: Algorithms and Hardness
We investigate the computational efficiency of multitask learning of Boo...
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08/25/2022
SNAP: Efficient Extraction of Private Properties with Poisoning
Property inference attacks allow an adversary to extract global properti...
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05/12/2022
How to Combine Membership-Inference Attacks on Multiple Updated Models
A large body of research has shown that machine learning models are vuln...
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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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06/24/2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
We present two sample-efficient differentially private mean estimators f...
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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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02/15/2021
Fair and Optimal Cohort Selection for Linear Utilities
The rise of algorithmic decision-making has created an explosion of rese...
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09/17/2020
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation
There has been a recent wave of interest in intermediate trust models fo...
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06/13/2020
Auditing Differentially Private Machine Learning: How Private is Private SGD?
We investigate whether Differentially Private SGD offers better privacy ...
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06/11/2020
CoinPress: Practical Private Mean and Covariance Estimation
We present simple differentially private estimators for the mean and cov...
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04/30/2020
A Primer on Private Statistics
Differentially private statistical estimation has seen a flurry of devel...
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04/23/2020
Private Query Release Assisted by Public Data
We study the problem of differentially private query release assisted by...
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02/21/2020
Private Mean Estimation of Heavy-Tailed Distributions
We give new upper and lower bounds on the minimax sample complexity of d...
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11/19/2019
The Power of Factorization Mechanisms in Local and Central Differential Privacy
We give new characterizations of the sample complexity of answering line...
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09/20/2019
Manipulation Attacks in Local Differential Privacy
Local differential privacy is a widely studied restriction on distribute...
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09/09/2019
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Learning the parameters of a Gaussian mixtures models is a fundamental a...
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05/28/2019
Private Identity Testing for High-Dimensional Distributions
In this work we present novel differentially private identity (goodness-...
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05/24/2019
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
We give a simple, computationally efficient, and node-differentially-pri...
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02/24/2019
Efficient Private Algorithms for Learning Halfspaces
We present new differentially private algorithms for learning a large-ma...
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12/06/2018
Differentially Private Fair Learning
We design two learning algorithms that simultaneously promise differenti...
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11/27/2018
The Structure of Optimal Private Tests for Simple Hypotheses
Hypothesis testing plays a central role in statistical inference, and is...
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08/04/2018
Distributed Differential Privacy via Mixnets
We consider the problem of designing scalable, robust protocols for comp...
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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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05/01/2018
Privately Learning High-Dimensional Distributions
We design nearly optimal differentially private algorithms for learning ...
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02/20/2018
Local Differential Privacy for Evolving Data
There are now several large scale deployments of differential privacy us...
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02/07/2018
