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Privacy and Trust Models for Cloud-Based EHRs Using Multilevel Cryptography and Artificial Intelligence

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Artificial Intelligence for Cloud and Edge Computing

Part of the book series: Internet of Things ((ITTCC))

Abstract

The pervasive as well as service-oriented nature of cloud computing has necessitated its adoption for development, deployment and servicing of Health Records Management solutions in today’s modern world. However, there are rapidly growing concerns for security/privacy and trustworthiness of such cloud-based systems/services. The dynamics of electronic health records (EHR) management system makes privacy preservation and trust, non-negotiable features of a presumably reliable cloud service. To cater for these concerns, this chapter proposes a multilevel cryptography approach for achieving security (privacy) in electronic health system. This approach involves using AES algorithm for on-premise encryption service and ECC for cloud-based encryption services towards ensuring data security/privacy at both the local hospitals’ electronic medical record (EMR) systems and the central cloud electronic health record (EHR) ends. Also, an artificial intelligence (AI) approach, which is referred to as subjective logic-based belief or reasoning model, was used towards measuring the trustworthiness of the medical personnel. Metrics such as the average system computation time otherwise referred to as the encryption/decryption runtime, system throughput, and system output size were utilized for appraising the privacy model in the newly developed system. The system was also compared with existing related systems in terms of standard reputation measurement metrics in order to determine its reputation performance. Results obtained from implementation shows that the proposed system offer better or highly improved performance in terms of security/privacy for users’ data, as well as reputation measurements.

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Authors and Affiliations

  1. Federal University of Technology, Akure, Nigeria

    Orobosade Alabi, Arome Junior Gabriel, Aderonke Thompson & Boniface Kayode Alese

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  1. Orobosade Alabi
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  2. Arome Junior Gabriel
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  3. Aderonke Thompson
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  4. Boniface Kayode Alese
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Correspondence to Arome Junior Gabriel .

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Editors and Affiliations

  1. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nandu, India

    Amit Kumar Tyagi

  3. Dipartimento di Informatica, Universita' degli Studi di Milano, Milano, Italy

    Vincenzo Piuri

  4. Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

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Alabi, O., Gabriel, A.J., Thompson, A., Alese, B.K. (2022). Privacy and Trust Models for Cloud-Based EHRs Using Multilevel Cryptography and Artificial Intelligence. In: Misra, S., Kumar Tyagi, A., Piuri, V., Garg, L. (eds) Artificial Intelligence for Cloud and Edge Computing. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-80821-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-80821-1_5

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