Classification under local differential privacy

Abstract

We consider the binary classification problem in a setup that preserves the privacy of the original sample. We provide a privacy mechanism that is locally differentially private and then construct a classifier based on the private sample that is universally consistent in Euclidean spaces. Under stronger assumptions, we establish the minimax rates of convergence of the excess risk and see that they are slower than in the case when the original sample is available.

12 pages

References in corpus (2)

• Fast learning rates for plug-in classifiers
• Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier