Secure Multiparty Computation for Generating Health Data Statistics

Abstract

Sharing of information by various individuals and organizations can provide a way for unlocking important and beneficial knowledge for all the parties involved. However, these information may be inherently confidential. Thus, there is a need for securely pooling these data while keeping their privacy. An important beneficiary of this situation is the medical field. Health data are inherently private, yet they are needed for information gathering, and the advancement of researches across many fields. The health data needed may be collected from databases, surveys, and other input sources. In this study, a health data statistics generation system was developed. It is a web-based tool that uses secure multiparty computation to aggregate answers from surveys published in the system.