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Privacy-preserving clinical decision support system using gaussian kernel-based classification
journal contribution
posted on 2017-02-02, 11:24 authored by Yogachandran RahulamathavanYogachandran Rahulamathavan, Suresh Veluru, Raphael C.-W. Phan, Jonathon Chambers, Muttukrishnan RajarajanA 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.
History
School
- Loughborough University London
Published in
IEEE Journal of Biomedical and Health InformaticsVolume
18Issue
1Pages
56 - 66Citation
RAHULAMATHAVAN, Y. ... et al, 2013. Privacy-preserving clinical decision support system using gaussian kernel-based classification. IEEE Journal of Biomedical and Health Informatics, 18 (1), pp. 56-66.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Publication date
2013Notes
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ISSN
2168-2194Publisher version
Language
- en
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