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A Linkable Identity Privacy Algorithm for HealthGrid

Zhang, N; Rector, A; Buchan, I; Shi, Q; Kalra, D; Rogers, J; Goble, C; ... Singleton, P; + view all (2005) A Linkable Identity Privacy Algorithm for HealthGrid. In: Solomonides, T and McClatchey, R, (eds.) UNSPECIFIED (234 - 245). I O S PRESS Green open access

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Abstract

The issues of confidentiality and privacy have become increasingly important as Grid technology is being adopted in public sectors such as healthcare. This paper discusses the importance of protecting the confidentiality and privacy of patient health/medical records, and the challenges exhibited in enforcing this protection in a Grid environment. It proposes a novel algorithm to allow traceable/linkable identity privacy in dealing with de-identified medical records. Using the algorithm, de-identified health records associated to the same patient but generated by different healthcare providers are given different pseudonyms. However, these pseudonymised records of the same patient can still be linked by a trusted entity such as the NHS trust or HealthGrid manager. The paper has also recommended a security architecture that integrates the proposed algorithm with other data security measures needed to achieve the desired security and privacy in the HealthGrid context.

Type: Book chapter
Title: A Linkable Identity Privacy Algorithm for HealthGrid
ISBN-13: 978-1-58603-510-5
Open access status: An open access version is available from UCL Discovery
Keywords: HealthGrid, security analysis, security architecture, privacy and accountability
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > CHIME
URI: https://discovery.ucl.ac.uk/id/eprint/1592
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