Secure Sound Classification: Gaussian Mixture Models

We propose secure protocols for Gaussian mixture-based sound recognition. The protocols we describe allow varying levels of security between two collaborating parties. The case we examine consists of one party (Alice) providing data and other party (Bob) providing a recognition algorithm. We show that it is possible to have Bob apply his algorithm on Alice's data in such a way that the data and the recognition results will not be revealed to Bob thereby guaranteeing Alice's data privacy. Likewise we show that it is possible to organize the collaboration so that a reverse engineering of Bob's recognition algorithm cannot be performed by Alice. We show how Gaussian mixtures can be implemented in a secure manner using secure computation primitives implementing simple numerical operations and we demonstrate the process by showing how it can yield identical results to a non-secure computation while maintaining privacy