This repository provides the implementation for the paper Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
We apply the generalized PTR framework to solve an open problem from the Private Aggregation of Teacher Ensembles (PATE) [Papernot et al., 2017, 2018] — privately publishing the entire model through privately releasing data-dependent DP losses. Our algorithm makes use of the smooth sensitivity framework [Nissim et al., 2007] and the Gaussian mechanism to construct a highprobability test of the data-dependent DP.
python pate.py