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 multipl...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2025-05-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/32/1/e101384.full |
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| author | Aziz Sheikh Cathie Sudlow Chris Robertson Steven Kerr |
| author_facet | Aziz Sheikh Cathie Sudlow Chris Robertson Steven Kerr |
| author_sort | Aziz Sheikh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e63261e617f6472abe0cd466e9acf67c |
| institution | OA Journals |
| issn | 2632-1009 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-e63261e617f6472abe0cd466e9acf67c2025-08-20T01:57:09ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092025-05-0132110.1136/bmjhci-2024-101384Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computationAziz Sheikh0Cathie Sudlow1Chris Robertson2Steven Kerr3Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UKThe University of Edinburgh, Edinburgh, UKDepartment of Mathematics and Statistics, University of Strathclyde, Glasgow, Glasgow, UKUsher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UKThe 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.https://informatics.bmj.com/content/32/1/e101384.full |
| spellingShingle | Aziz Sheikh Cathie Sudlow Chris Robertson Steven Kerr Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation BMJ Health & Care Informatics |
| title | Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation |
| title_full | Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation |
| title_fullStr | Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation |
| title_full_unstemmed | Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation |
| title_short | Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation |
| title_sort | enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation |
| url | https://informatics.bmj.com/content/32/1/e101384.full |
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