Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation

The UK's health datasets are among the most comprehensive and inclusive globally, enabling groundbreaking research during the COVID-19 pandemic. However, restrictions on data sharing between secure data environments (SDEs) imposed limitations on the ability to carry out joint analyses across multiple separate datasets. There are currently significant efforts underway to enable such analyses using methods such as federated analytics (FA) and virtual SDEs. FA involves distributed data analysis without sharing raw data but does require sharing summary statistics. Virtual SDEs in principle allow researchers to access data across multiple SDEs, but in practice, data transfers may be restricted by information governance concerns.Secure multiparty computation (SMPC) is a cryptographic approach that allows multiple parties to perform joint analyses over private datasets with zero information sharing. SMPC may eliminate the need for data-sharing agreements and statistical disclosure control, offering a compelling alternative to FA and virtual SDEs. SMPC comes with a higher computational burden than traditional pooled analysis. However, efficient implementations of SMPC can enable a wide range of practical, secure analyses to be carried out.This perspective reviews the strengths and limitations of FA, virtual SDEs and SMPC as approaches to joint analyses across SDEs. We argue that while efforts to implement FA and virtual SDEs are ongoing in the UK, SMPC remains underexplored. Given its unique advantages, we propose that SMPC deserves greater attention as a transformative solution for enabling secure, cross-SDE analyses of private health data.