5 papers
Near-Optimal Pure Machine Unlearning for Smooth Strongly Convex Losses
Matthew Regehr, Gautam Kamath, Andrew Lowy
Machine unlearning is motivated by legal and user-facing requirements to remove the influence of individuals' data from trained models, such as the right to be forgotten. Prior wor…
Privacy Filters are Captured by Residues: A Characterization of Free Natural Filters and the Cost of Adaptivity
Matthew Regehr, Bingshan Hu, Ethan Leeman +3
We study privacy filters, which enable privacy accounting for differentially private (DP) mechanisms with adaptively chosen privacy characteristics. We develop a general theory tha…
Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph
Gautam Kamath, Alireza F. Pour, Matthew Regehr +1
We propose an algorithm with improved query-complexity for the problem of hypothesis selection under local differential privacy constraints. Given a set of probability distribu…
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
Christian Janos Lebeda, Matthew Regehr, Gautam Kamath +1
We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the pri…
A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
Gautam Kamath, Argyris Mouzakis, Matthew Regehr +3
Differential privacy (DP) is a rigorous notion of data privacy, used for private statistics. The canonical algorithm for differentially private mean estimation is to first clip the…