Differentially Private Dataset Condensation

Tianhang Zheng, Baochun Li

NDSS 2024 Workshop · first public 2024-01-01

paper ↗catalogued✓ abstract read

In one paragraph

Proposes two differentially-private dataset condensation algorithms: LDPDC, a linear DC method that runs on a low-end CPU, and NDPDC, which uses a neural network for representation extraction under a DP feature/gradient-matching objective. Reports LDPDC performs comparably to recent privacy-preserving generative methods, while NDPDC gives acceptable DP guarantees with only mild utility loss relative to plain distribution matching.

Where it sits

Gradient matching (Surrogate matching)
Distribution and feature matching (Surrogate matching)
Setting: Image classification

BibTeX (generated; prefer the venue's official entry)

@article{zheng2024differentially,
  title   = {Differentially Private Dataset Condensation},
  author  = {Tianhang Zheng and Baochun Li},
  journal = {NDSS 2024 Workshop},
  year    = {2024}
}

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