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Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Lookup NU author(s): Professor Jonathon Chambers


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A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised.

Publication metadata

Author(s): Rahulamathavan Y, Veluru S, Phan RC-W, Rajarajan M, Chambers JA

Publication type: Article

Publication status: Published

Journal: IEEE Journal of Biomedical and Health Infomatics

Year: 2014

Volume: 18

Issue: 1

Pages: 56-66

Print publication date: 01/01/2014

Online publication date: 25/07/2013

Acceptance date: 17/07/2013

ISSN (print): 2168-2194

ISSN (electronic): 2168-2208

Publisher: Institute of Electrical and Electronics Engineers


DOI: 10.1109/JBHI.2013.2274899

PubMed id: 24403404


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