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Found 4 papers, 0 papers with code
Not All Learnable Distribution Classes are Privately Learnable
We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not learnable under $(\varepsilon, \delta)$-differential privacy.
A Bias-Variance-Privacy Trilemma for Statistical Estimation
The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean.
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
First, we provide tight lower bounds for private covariance estimation of Gaussian distributions.
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
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 $\mathbb{R}^d$.
