560 citations · 2.1k across the 38 of their papers we have counts for
3 papers · 1 filter
Scalable Membership Inference Attacks via Quantile Regression
Martin Bertran, Shuai Tang, Michael Kearns +3
Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can…
Balanced Filtering via Disclosure-Controlled Proxies
Siqi Deng, Emily Diana, Michael Kearns +1
We study the problem of collecting a cohort or set that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at deployment time.…
Improved Differentially Private Regression via Gradient Boosting
Shuai Tang, Sergul Aydore, Michael Kearns +5
We revisit the problem of differentially private squared error linear regression. We observe that existing state-of-the-art methods are sensitive to the choice of hyperparameters -…