METHOD AND SYSTEM FOR OPTIMAL SELECTION OF PARAMETERS FOR PRIVACY PRESERVING MACHINE LEARNING APPLICATIONS USING FULLY HOMOMORPHIC ENCRYPTION

摘要

The disclosure herein generally relates to the field of privacy preserving in an application, and, more particularly, to enabling privacy in an application using fully homomorphic encryption. The disclosure more specifically refers to enabling a most optimal FHE for privacy preserving for the application based on a set of constraints using a disclosed set of optimization tasks. The set of optimization tasks comprise a multi objective-multi constraint optimization task and a single objective- multi constraint optimization task, that identifies an optimal FHE library, along with an associated FHE functionality and an optimal configuration of the associated FHE functionality based on the set of constraints. The identified FHE library along with the associated FHE functionality and the optimal configuration of the associated FHE functionality facilitate optimal implementation of privacy in the applications.