
Private Counting from Anonymous Messages: NearOptimal Accuracy with Vanishing Communication Overhead
Differential privacy (DP) is a formal notion for quantifying the privacy...
read it

Locally Private kMeans in One Round
We provide an approximation algorithm for kmeans clustering in the one...
read it

On Deep Learning with Label Differential Privacy
In many machine learning applications, the training data can contain hig...
read it

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...
read it

Sampleefficient proper PAC learning with approximate differential privacy
In this paper we prove that the sample complexity of properly learning a...
read it

Robust and Private Learning of Halfspaces
In this work, we study the tradeoff between differential privacy and ad...
read it

On Distributed Differential Privacy and Counting Distinct Elements
We study the setup where each of n users holds an element from a discret...
read it

Differentially Private Clustering: Tight Approximation Ratios
We study the task of differentially private clustering. For several basi...
read it

Neartight closure bounds for Littlestone and threshold dimensions
We study closure properties for the Littlestone and threshold dimensions...
read it

Pure Differentially Private Summation from Anonymous Messages
The shuffled (aka anonymous) model has recently generated significant in...
read it

Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
read it

Private Aggregation from Fewer Anonymous Messages
Consider the setup where n parties are each given a number x_i ∈F_q and ...
read it

Private Heavy Hitters and Range Queries in the Shuffled Model
An exciting new development in differential privacy is the shuffled mode...
read it

Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Federated learning promises to make machine learning feasible on distrib...
read it

Recursive Sketches for Modular Deep Learning
We present a mechanism to compute a sketch (succinct summary) of how a c...
read it

CommunicationRounds Tradeoffs for Common Randomness and Secret Key Generation
We study the role of interaction in the Common Randomness Generation (CR...
read it

Dimension Reduction for Polynomials over Gaussian Space and Applications
We introduce a new technique for reducing the dimension of the ambient s...
read it

ResourceEfficient Common Randomness and SecretKey Schemes
We study common randomness where two parties have access to i.i.d. sampl...
read it
Badih Ghazi
is this you? claim profile