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A Distributed Multi-Authority Attribute Based Encryption Scheme for Secure Sharing of Personal Health Records

Published:07 June 2017Publication History

ABSTRACT

Personal health records (PHR) are an emerging health information exchange model, which facilitates PHR owners to efficiently manage their health data. Typically, PHRs are outsourced and stored in third-party cloud platforms. Although, outsourcing private health data to third-party platforms is an appealing solution for PHR owners, it may lead to significant privacy concerns, because there is a higher risk of leaking private data to unauthorized parties. As a way of ensuring PHR owners' control of their outsourced PHR data, attribute based encryption (ABE) mechanisms have been considered due to the fact that such schemes facilitate a mechanism of sharing encrypted data among a set of intended recipients. However, such existing PHR solutions suffer from inflexibility and scalability issues due to the limitations associated with the adopted ABE mechanisms. To address these issues, we propose a distributed multi-authority ABE scheme and thereby we show how a patient-centric, attribute based PHR sharing scheme which can provide flexible access for both professional users such as doctors as well as personal users such as family and friends is realized. We have shown that the proposed scheme supports on-demand user revocation as well as secure under standard security assumptions. In addition, the simulation results provide evidence for the fact that our scheme can function efficiently in practice. Furthermore, we have shown that the proposed scheme can cater the access requirements associated with distributed multiuser PHR sharing environments as well as more realistic and scalable compared with similar existing PHR sharing schemes.

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            cover image ACM Conferences
            SACMAT '17 Abstracts: Proceedings of the 22nd ACM on Symposium on Access Control Models and Technologies
            June 2017
            276 pages
            ISBN:9781450347020
            DOI:10.1145/3078861

            Copyright © 2017 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 7 June 2017

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            Acceptance Rates

            SACMAT '17 Abstracts Paper Acceptance Rate14of50submissions,28%Overall Acceptance Rate177of597submissions,30%

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