279 citations · 1.4k across the 46 of their papers we have counts for
6 papers · 1 filter
Jointly Private Convex Programming
Justin Hsu, Zhiyi Huang, Aaron Roth +1
In this paper we present an extremely general method for approximately solving a large family of convex programs where the solution can be divided between different agents, subject…
Proving differential privacy in Hoare logic
Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias +3
Differential privacy is a rigorous, worst-case notion of privacy-preserving computation. Informally, a probabilistic program is differentially private if the participation of a sin…
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy
Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias +3
Mechanism design is the study of algorithm design in which the inputs to the algorithm are controlled by strategic agents, who must be incentivized to faithfully report them. Unlik…
Differential Privacy: An Economic Method for Choosing Epsilon
Justin Hsu, Marco Gaboardi, Andreas Haeberlen +4
Differential privacy is becoming a gold standard for privacy research; it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assump…
Privately Solving Linear Programs
Justin Hsu, Aaron Roth, Tim Roughgarden +1
In this paper, we initiate the systematic study of solving linear programs under differential privacy. The first step is simply to define the problem: to this end, we introduce sev…
Dual Query: Practical Private Query Release for High Dimensional Data
Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu +2
We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets. Like all algorithms for this task, ours necessarily h…