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Title: Secure Multiparty Computation for Generating Health Data Statistics
Authors: Chua, Richard Bryann L.
Navarro, Jairus Mari H.
Keywords: secure multiparty computation
health data
Issue Date: Jun-2015
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.
Appears in Collections:Computer Science SP

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