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cs.LG2018
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao +4
Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attri…
cs.LG2018
Locally Private Gaussian Estimation
Matthew Joseph, Janardhan Kulkarni, Jieming Mao +1
We study a basic private estimation problem: each of users draws a single i.i.d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaus…