5 papers
Privately Clipping Heavy-Tailed Data
Matthew Joseph, Alex Kulesza
Summing vectors is a basic task in differentially private data analysis. Standard private sum algorithms clip vectors to some bound, sum them, and add noise scaled to the bound. Th…
Missing Mass for Differentially Private Domain Discovery
Travis Dick, Matthew Joseph, Vinod Raman
We study several problems in differentially private domain discovery, where each user holds a subset of items from a shared but unknown domain, and the goal is to output an informa…
Approximate Differential Privacy of the Mechanism
Matthew Joseph, Alex Kulesza, Alexander Yu
We study the mechanism for computing a -dimensional statistic with bounded sensitivity under approximate differential privacy. Across a range of privacy parame…
Privately Counting Partially Ordered Data
Matthew Joseph, Mónica Ribero, Alexander Yu
We consider differentially private counting when each data point consists of bits satisfying a partial order. Our main technical contribution is a problem-specific -norm mec…
Some Constructions of Private, Efficient, and Optimal -Norm and Elliptic Gaussian Noise
Matthew Joseph, Alexander Yu
Differentially private computation often begins with a bound on some -dimensional statistic's sensitivity. For pure differential privacy, the -norm mechanism can imp…