4 papers
Privately Estimating Monotone Statistics in Polynomial Time
Gavin Brown, Ephraim Linder, Mahbod Majid +1
We study efficient differentially private algorithms for estimating monotone statistics, i.e., statistics that are monotone under the addition of new observations. The starting poi…
Refereed Learning
Ran Canetti, Ephraim Linder, Connor Wagaman
We initiate an investigation of learning tasks in a setting where the learner is given access to two competing provers, only one of which is honest. Specifically, we consider the p…
Privately Evaluating Untrusted Black-Box Functions
Ephraim Linder, Sofya Raskhodnikova, Adam Smith +1
We provide tools for sharing sensitive data when the data curator does not know in advance what questions an (untrusted) analyst might ask about the data. The analyst can specify a…
Online versus Offline Adversaries in Property Testing
Esty Kelman, Ephraim Linder, Sofya Raskhodnikova
We study property testing with incomplete or noisy inputs. The models we consider allow for adversarial manipulation of the input, but differ in whether the manipulation can be don…