REVIEW 2 cited by
Differentially Private Learning Needs Better Features (or Much More Data)
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
read the original abstract
We demonstrate that differentially private machine learning has not yet reached its "AlexNet moment" on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets. To exceed the performance of handcrafted features, we show that private learning requires either much more private data, or access to features learned on public data from a similar domain. Our work introduces simple yet strong baselines for differentially private learning that can inform the evaluation of future progress in this area.
Forward citations
Cited by 2 Pith papers
-
Generalization and Memorization in Rectified Flow
Rectified Flow models peak in membership-inference vulnerability at the flow midpoint under uniform training; U-shaped timestep sampling suppresses memorization without harming FID.
-
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy
A private graph embedding method that claims to preserve user-chosen node proximities, though its central proof and privacy analysis contain serious gaps.
Discussion (0). Continue with ORCID to comment.