Introduction to secret sharing based secure multiparty computation in health data.

Abstract

Joint analyses across multiple health datasets can increase statistical power and improve the generalisability of research findings. However, limitations on data sharing often prevent researchers from fully realising these benefits. Existing approaches such as federated analytics involve sharing information, which poses challenges due to data governance and security restrictions.Secure multiparty computation (SMPC) is a set of cryptographic techniques that allows joint analyses across multiple private datasets with zero information sharing except for the agreed outputs. Despite its transformative potential in health research, SMPC has received relatively little attention within the health data landscape. This article gives an introduction to secret sharing based SMPC that is accessible with no prior knowledge assumed. We explain how secret sharing techniques work, and the security guarantees they offer. We also discuss SMPC software, and offer our view on the most promising approaches to implementation. SMPC has significant potential for enabling privacy-preserving analyses, and could become a standard tool in the future for collaborative health data research. As efforts to improve data access and integration continue, it will be increasingly important for health data researchers to have an understanding of SMPC so they can use it effectively.