Private Estimation with Public Data. (arXiv:2208.07984v1 [cs.LG])

We initiate the study of differentially private (DP) estimation with access
to a small amount of public data. For private estimation of d-dimensional
Gaussians, we assume that the public data comes from a Gaussian that may have
vanishing similarity in total variation distance with the underlying Gaussian
of the private data. We show that under the constraints of pure or concentrated
DP, d+1 public data samples are sufficient to remove any dependence on the
range parameters of the private data …

data lg public