  We prove lower bounds on the number of samples needed to privately estimate
the covariance matrix of a Gaussian distribution. Our bounds match existing
upper bounds in the widest known setting of parameters. Our analysis relies on
the Stein-Haff identity, an extension of the classical Stein's identity used in
previous fingerprinting lemma arguments.
