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Gap-free Differentially Private PCA for Gaussian Data
AI summary · grok-4.7Summaries by Vani AI
Researchers present a gap-free differentially private PCA algorithm for Gaussian data.
The paper states a gap-free differentially private algorithm for principal component analysis on Gaussian data. The available abstract does not report privacy parameters, sample complexity, error bounds, or experimental results.
Key facts
- Proposes a gap-free differentially private PCA algorithm.
- The stated setting is Gaussian data.
- The abstract reports no privacy budget or error bounds.
From the original article
1 min read at arxiv.orgWe give a gap-free differentially private algorithm for the principal component analysis (PCA) problem with Gaussian data.
By Alina Ene · arxiv.org
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