A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia

• Published in: Journal of biomedical informatics 2023-03, Vol.139, p.104322-104322, Article 104322
• Main Authors: Ohno-Machado, Lucila, Jiang, Xiaoqian, Kuo, Tsung-Ting, Tao, Shiqiang, Chen, Luyao, Ram, Pritham M., Zhang, Guo-Qiang, Xu, Hua
• Format: Article
• Language: English
• Subjects: Biomedical Research; Computer Security; Humans; Privacy
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2023.104322

[Display omitted] Linking data across studies offers an opportunity to enrich data sets and provide a stronger basis for data-driven models for biomedical discovery and/or prognostication. Several techniques to link records have been proposed, and some have been implemented across data repositories holding molecular and clinical data. Not all these techniques guarantee appropriate privacy protection; there are trade-offs between (a) simple strategies that can be associated with data that will be linked and shared with any party and (b) more complex strategies that preserve the privacy of individuals across parties. We propose an intermediary, practical strategy to support linkage in studies that share de-identified data with Data Coordinating Centers. This technology can be extended to link data across multiple data hubs to support privacy preserving record linkage, considering data coordination centers and their awardees, which can be extended to a hierarchy of entities (e.g., awardees, data coordination centers, data hubs, etc.) b.