381 citations · 2.3k across the 89 of their papers we have counts for
8 papers · 1 filter
Exploiting Metric Structure for Efficient Private Query Release
Zhiyi Huang, Aaron Roth
We consider the problem of privately answering queries defined on databases which are collections of points belonging to some metric space. We give simple, computationally efficien…
Efficiently Learning from Revealed Preference
Morteza Zadimoghaddam, Aaron Roth
In this paper, we consider the revealed preferences problem from a learning perspective. Every day, a price vector and a budget is drawn from an unknown distribution, and a rationa…
Beyond Worst-Case Analysis in Private Singular Vector Computation
Moritz Hardt, Aaron Roth
We consider differentially private approximate singular vector computation. Known worst-case lower bounds show that the error of any differentially private algorithm must scale pol…
Differential Privacy for the Analyst via Private Equilibrium Computation
Justin Hsu, Aaron Roth, Jonathan Ullman
We give new mechanisms for answering exponentially many queries from multiple analysts on a private database, while protecting differential privacy both for the individuals in the…
Mechanism Design in Large Games: Incentives and Privacy
Michael Kearns, Mallesh M. Pai, Aaron Roth +1
We study the problem of implementing equilibria of complete information games in settings of incomplete information, and address this problem using "recommender mechanisms." A reco…
Conducting Truthful Surveys, Cheaply
Aaron Roth, Grant Schoenebeck
We consider the problem of conducting a survey with the goal of obtaining an unbiased estimator of some population statistic when individuals have unknown costs (drawn from a known…