We propose improving the privacy properties of a dataset by publishing only a strategically chosen "core-set" of the data containing a subset of the instances. The core-set allows strong performance on primary tasks, but forces poor performance on unwanted tasks. We give methods for both linear models and neural networks and demonstrate their efficacy on data.
@article{arxiv.1910.10871,
title = {Preventing Adversarial Use of Datasets through Fair Core-Set Construction},
author = {Benjamin Spector and Ravi Kumar and Andrew Tomkins},
journal= {arXiv preprint arXiv:1910.10871},
year = {2019}
}