Differentially Private Algorithms for Learning Mixtures of Separated Gaussians

In this work, westudy algorithms for learning Gaussian mixtures subject todifferential privacy[32], which has become thede facto standard for individual privacy in statistical analysis of sensitive data. Intuitively, differential privacy guarantees that the output of the algorithm does not depend significantly on any one individual's data, which in this case means any one sample.