Pith. sign in

REVIEW

Preventing Adversarial Use of Datasets through Fair Core-Set Construction

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1910.10871 v1 pith:N2LWZ2BV submitted 2019-10-24 cs.LG cs.AIstat.ML

Preventing Adversarial Use of Datasets through Fair Core-Set Construction

classification cs.LG cs.AIstat.ML
keywords core-setdataperformancetasksadversarialallowschosenconstruction
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.