A Privacy Preserving Framework for Gaussian Mixture Models

This paper presents a framework for privacy-preserving Gaussian Mixture Model computations. Specifically, we consider a scenario where a central service wants to learn the parameters of a Guassian Mixture Model from private data distributed among multiple parties with privacy constraints. In addition, the service also has security constraints where none of the data owners are allowed to learn the values of the trained parameters. We use Secure Multiparty Computations to propose a framework that allows such computations. In addition, we also show how such a central service can classify new test data from privacy constrained third parties without exposing the learned models. The classification occurs with the added constraint that the service learns no information about either the test data or the result of the classification.