Private Everlasting Prediction. (arXiv:2305.09579v1 [cs.LG])

A private learner is trained on a sample of labeled points and generates a
hypothesis that can be used for predicting the labels of newly sampled points
while protecting the privacy of the training set [Kasiviswannathan et al., FOCS
2008]. Research uncovered that private learners may need to exhibit
significantly higher sample complexity than non-private learners as is the case
with, e.g., learning of one-dimensional threshold functions [Bun et al., FOCS
2015, Alon et al., STOC 2019].

We explore prediction …

complexity higher may non prediction privacy private protecting research training