Maintaining Security of Patient Data by Employing Private Blockchain and Fog Computing Technologies based on Internet of Medical Things

Mishall H Al-Zubaidie, Rasha Halim Razzaq

Abstract


The Internet of Medical Things (IoMT) is a vital component of the Internet of Things (IoT), and its importance lies in the urgent need for it and its provision of many medical services, such as examining and monitoring patients in hospitals and their homes. Given the presence of huge amounts of data based on the IoMT in the cloud system, data storage methods should witness a major revolution, and given the exposure of IoMT systems to electronic attacks, as recent studies have indicated, which makes them unsafe, data must be protected with security systems. In our work, we propose a Cryptography Health Security System (CryptoHSS) to support medical IoT security. Our proposed CryptoHSS relies on Decision Tree (DT), Naive Bayes (NB), Two-Fish, and Jellyfish algorithms within Private Blockchain (PBC) and Fog Computing to build robust security measures. The Two-Fish encryption algorithm is used to provide anonymity of medical information. In our proposed system, NB is used to quickly classify patient data, while DT is used to make accurate medical decisions based on the collected data. The Jellyfish algorithm was used to detect similarities between data and increase the security of data transmission within CryptoHSS. Two-Fish, NB, DT, and Jellyfish algorithms are designed to work in harmony with PBC. CryptoHSS distributes and manages peer-to-peer data in IoMT. The benefit of Fog Computing (FC) is that it speeds up the decision-making process without moving to distant clouds. We analyzed our system in terms of performance and security. Our results indicate that CryptoHSS provides lightweight operations to support complex security measures that qualify it to support health organizations. In terms of security, our system provides reliable security against attacks by keeping medical data encrypted and confidential, with the encryption and decryption rate with the Two-Fish algorithm reaching more than 98%, in addition to providing diagnosis of medical conditions and making appropriate medical decisions.

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DOI: https://doi.org/10.31449/inf.v48i12.6047

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