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Vol. 5, July  Issue 7

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Vol. 5,  Special Issue(Bi-yearly)

IMPACT FACTOR 5.909 (2018)

UGC Listed Journal(2017) 


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DEEP LEARNING APPROACH FOR CLINICAL RISK PREDICTION USING ELECTRONICHEALTH RECORDS


Abstract

Abstract: Thebroad acknowledgment of cloud based services in the healthcare sector has broughtabout practical and helpful trade of Personal Health Records (PHRs) among a fewtaking part elements of the e-Health systems. Nevertheless, putting away the secrethealth data to cloud servers is susceptible to revelation or theft and requiresthe improvement of approaches that guarantee the protection of the PHRs. Alongthese lines; we propose an approach called SeSPHR for secure sharing of thePHRs in the cloud. The SeSPHR scheme ensures patient-centric control on thePHRs and preserves the classification of the PHRs. The patients store theencrypted PHRs on the un-trusted in cloud servers and specifically grant accessto various kinds of clients on various parts of the PHRs. A semi-trusted inproxy called Setup and Re-encryption Server (SRS) is acquainted with set uppeople in general/private key combines and to deliver the re-encryption keys.Besides, the strategy is secure against insider threats and furthermoreauthorizes a forward and in reverse access control. Moreover, we formallyexamine and check the working of SeSPHR strategy through the High Level PetriNets (HLPN). Execution assessment in regards to time utilization demonstrates thatthe SeSPHR strategy can possibly be utilized for safely sharing the PHRs in thecloud.

Keywords:Cloud Computing, PersonalHealth Records, Privacy application.


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Department of Civil Engineering, Amity University Haryana,




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