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09/27/2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
The shuffle model of differential privacy has attracted attention in the...
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08/03/2021
Large-Scale Differentially Private BERT
In this work, we study the large-scale pretraining of BERT-Large with di...
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07/02/2021
Google COVID-19 Vaccination Search Insights: Anonymization Process Description
This report describes the aggregation and anonymization process applied ...
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06/08/2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Differential privacy (DP) is a formal notion for quantifying the privacy...
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04/20/2021
Locally Private k-Means in One Round
We provide an approximation algorithm for k-means clustering in the one-...
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02/11/2021
On Deep Learning with Label Differential Privacy
In many machine learning applications, the training data can contain hig...
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12/16/2020
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries
In this work, we study the problem of answering k queries with (ϵ, δ)-di...
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12/07/2020
Sample-efficient proper PAC learning with approximate differential privacy
In this paper we prove that the sample complexity of properly learning a...
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11/30/2020
Robust and Private Learning of Halfspaces
In this work, we study the trade-off between differential privacy and ad...
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09/21/2020
On Distributed Differential Privacy and Counting Distinct Elements
We study the setup where each of n users holds an element from a discret...
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08/18/2020
Differentially Private Clustering: Tight Approximation Ratios
We study the task of differentially private clustering. For several basi...
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07/07/2020
Near-tight closure bounds for Littlestone and threshold dimensions
We study closure properties for the Littlestone and threshold dimensions...
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02/05/2020
Pure Differentially Private Summation from Anonymous Messages
The shuffled (aka anonymous) model has recently generated significant in...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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09/24/2019
Private Aggregation from Fewer Anonymous Messages
Consider the setup where n parties are each given a number x_i ∈F_q and ...
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08/29/2019
Private Heavy Hitters and Range Queries in the Shuffled Model
An exciting new development in differential privacy is the shuffled mode...
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06/19/2019
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Federated learning promises to make machine learning feasible on distrib...
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05/29/2019
Recursive Sketches for Modular Deep Learning
We present a mechanism to compute a sketch (succinct summary) of how a c...
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08/27/2018
Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation
We study the role of interaction in the Common Randomness Generation (CR...
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08/12/2017
Dimension Reduction for Polynomials over Gaussian Space and Applications
We introduce a new technique for reducing the dimension of the ambient s...
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07/25/2017