Gap-free Differentially Private PCA for Gaussian Data

Alina Ene, Huy L. Nguyen

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.