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Enabling genomic-phenomic association discovery without sacrificing anonymity

Heatherly, Raymond D., Loukides, Grigorios, Denny, Joshua C., Haines, Jonathan L., Roden, Dan M. and Malin, Bradley A. 2013. Enabling genomic-phenomic association discovery without sacrificing anonymity. PLoS ONE 8 (2) , e53875. 10.1371/journal.pone.0053875

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Abstract

Health information technologies facilitate the collection of massive quantities of patient-level data. A growing body of research demonstrates that such information can support novel, large-scale biomedical investigations at a fraction of the cost of traditional prospective studies. While healthcare organizations are being encouraged to share these data in a de-identified form, there is hesitation over concerns that it will allow corresponding patients to be re-identified. Currently proposed technologies to anonymize clinical data may make unrealistic assumptions with respect to the capabilities of a recipient to ascertain a patients identity. We show that more pragmatic assumptions enable the design of anonymization algorithms that permit the dissemination of detailed clinical profiles with provable guarantees of protection. We demonstrate this strategy with a dataset of over one million medical records and show that 192 genotype-phenotype associations can be discovered with fidelity equivalent to non-anonymized clinical data.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: PLoS
ISSN: 1932-6203
Date of First Compliant Deposit: 30 March 2016
Last Modified: 12 Jun 2019 02:52
URI: https://orca.cardiff.ac.uk/id/eprint/44060

Citation Data

Cited 21 times in Scopus. View in Scopus. Powered By Scopus® Data

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