  We give the first polynomial-time, polynomial-sample, differentially private
estimator for the mean and covariance of an arbitrary Gaussian distribution
$\mathcal{N}(\mu,\Sigma)$ in $\mathbb{R}^d$. All previous estimators are either
nonconstructive, with unbounded running time, or require the user to specify a
priori bounds on the parameters $\mu$ and $\Sigma$. The primary new technical
tool in our algorithm is a new differentially private preconditioner that takes
samples from an arbitrary Gaussian $\mathcal{N}(0,\Sigma)$ and returns a matrix
$A$ such that $A \Sigma A^T$ has constant condition number.
