A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input

We study the problem of differentially private second moment estimation and present a new algorithm that achieve strong privacy-utility trade-offs even for worst-case inputs under subsamplability assumptions on the data.

We call an input $(m,\alpha ,\beta )$-subsamplable if a random subsample of size $m$ (or larger) preserves w.p $\ge 1-\beta$ the spectral structure of the original second moment matrix up to a multiplicative factor of $1\pm \alpha$.

Building upon subsamplability, we give a recursive algorithmic framework similar to Kamath et al (2019) that abides zero-Concentrated Differential Privacy (zCDP) while preserving w.h.p the accuracy of the second moment estimation upto an arbitrary factor of $(1\pm \gamma )$. We then show how to apply our algorithm to approximate the second moment matrix of a distribution $\mathcal{D}$, even when a noticeable fraction of the input are outliers.