1 research outputs found
Preventing Adversarial Use of Datasets through Fair Core-Set Construction
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.Comment: 6 pages, 2 figures, NeurIPS 2019 Privacy In Machine Learning Workshop
(PriML 2019