PRISED tangle: A privacy-aware framework for smart healthcare data sharing using IOTA tangle

Sidrah Abdullah, Junaid Arshad, Muhammad Mubashir Khan, Mamoun Alazab, Khaled Salah

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)
    55 Downloads (Pure)


    Healthcare has evolved significantly in recent years primarily due to the advancements in and increasing adoption of technology in healthcare processes such as data collection, storage, diagnostics, and treatment. The emergence of the industrial internet of things (IIoT) has further evolved e-Health by facilitating the development of connected healthcare systems which can significantly improve data connectivity, visibility, and interoperability leading to improved quality of service delivered to patients. However, such technological advancements come with their perils—there are growing concerns with regards to the security and privacy of healthcare data especially when collected, shared, and processed using cutting-edge connected sensor devices affecting the adoption of next-generation e-healthcare systems. In particular, during the front-end and back-end data transfer in health information exchange (HIE) there exist a security risk in term of confidentiality, integrity, authentication and access control of the data due to the limited capabilities of IoT devices involved. In this paper, we investigate the use of distributed ledger technologies (DLT) to address such security concerns for emerging healthcare systems. In particular, we use masked authenticated messaging (MAM) over the Tangle to achieve secure data sharing within a healthcare system and provide a proof-of-concept of applying the proposed approach for securing healthcare data in a connected IIoT environment. Further, we have performed the evaluation and analysis of data communication against the metrics of encryption and efficiency in transaction time.

    Original languageEnglish
    Pages (from-to)3023-3041
    Number of pages19
    JournalComplex and Intelligent Systems
    Issue number3
    Early online date2022
    Publication statusPublished - Jun 2023


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