Paper · arXiv
Gap-free Differentially Private PCA for Gaussian Data
We give a gap-free $(ε,δ)$-differentially private algorithm for the principal component analysis (PCA) problem with Gaussian data. The algorithm is based on a private variant of the power iteration method, and it is computationally efficient.
| Authors | Alina Ene, Huy L. Nguyen |
|---|---|
| Primary category | cs.DS |
| Published | 25 September 2026 |
| Last updated at source | 29 September 2026 |
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