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Differentially Private Multi-label Learning Is Harder Than You'd Think

This repository contains the code for the paper "Differentially Private Multi-label Learning Is Harder Than You'd Think" by Benjamin Friedl and Anika Hannemann.

Aggregation.py implements the 3 aggregation mechanisms GNThreshold, Confident GNThreshold and Interactive GNThreshold. These are PATE methods generalized for multi-label classification. Privacy.py contains the privacy analysis.

Installation can be done via pip install multilabelpate

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