REVIEW 1 cited by
Regression with Label Differential Privacy
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
Signed reviews
read the original abstract
We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained privately, we derive a label DP randomization mechanism that is optimal under a given regression loss function. We prove that the optimal mechanism takes the form of a "randomized response on bins", and propose an efficient algorithm for finding the optimal bin values. We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm.
Forward citations
Cited by 1 Pith paper
-
Aggregating Data for Optimal and Private Learning
For multiple instance regression and learning from label proportions, optimal data bagging for linear regression reduces approximately to k-means clustering over labels or features, and the mechanisms can be made labe...
Discussion (0). Continue with ORCID to comment.