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Review
. 2023 Aug 25:24:347-368.
doi: 10.1146/annurev-genom-110122-084756. Epub 2023 May 30.

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

James Casaletto  1 , Alexander Bernier  2 , Robyn McDougall  2 , Melissa S Cline  1
Affiliations
Review

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

James Casaletto et al. Annu Rev Genomics Hum Genet. .

Abstract

Continued advances in precision medicine rely on the widespread sharing of data that relate human genetic variation to disease. However, data sharing is severely limited by legal, regulatory, and ethical restrictions that safeguard patient privacy. Federated analysis addresses this problem by transferring the code to the data-providing the technical and legal capability to analyze the data within their secure home environment rather than transferring the data to another institution for analysis. This allows researchers to gain new insights from data that cannot be moved, while respecting patient privacy and the data stewards' legal obligations. Because federated analysis is a technical solution to the legal challenges inherent in data sharing, the technology and policy implications must be evaluated together. Here, we summarize the technical approaches to federated analysis and provide a legal analysis of their policy implications.

Keywords: GDPR; federated analysis; federation; genomics; privacy protection.

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Figures

Figure 1
Figure 1
Illustrating the logic of “bringing the code to the data,” either because the data are much larger relative to the analytical code [f(Data)] or because of restrictions on exporting the data across organizational boundaries.
Figure 2
Figure 2
The General Data Protection Regulation: roles and responsibilities.
Figure 3
Figure 3
Different ways in which data are analyzed to build models and databases. (a) Siloed datasets used for local analysis, where models and databases do not contain other datasets. (b) Centralized datasets exported from multiple organizations to a single place where models and databases are built. (c) Data federated from multiple institutions to build a single model or database.
Figure 4
Figure 4
Trust architectures. (a) A centralized trust running external to the federation. (b) A decentralized trust in which any central coordination is distributed across the cluster. (c) A nonclustered environment in which trust is established pairwise between the data consumer and each data owner.
See this image and copyright information in PMC

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