The Internet of Medical Things (IoMT) combines medical devices and applications that use network technologies to connect healthcare information technology systems (HITs). IoMT is reforming the medical industry by adopting information and communication technologies (ICTs). Identity verification, secure collection, and exchange of medical data are essential in health applications. In this study, we implemented a hybrid security solution (SFTSDH = SF + TSD + H) to secure the collection and management of personal health data using Spring Framework (SF), Services for Sensitive Data (TSD) as a service platform, and Hyper-Text-Transfer-Protocol (HTTP) security methods. The adopted solution instigated the following security features: identity brokering, OAuth2, multifactor authentication, and access control to protect the Microservice Architecture Application Programming Interfaces (APIs), following the General Data Protection Regulation (GDPR). Moreover, we extended the adopted security solution to develop a digital infrastructure to facilitate the research and innovation work in the electronic health (eHealth) section, focusing on solution validation with theoretical evaluation and experimental testing. To achieve and explain the adopted security solution, we used a web engineering security methodology. As a case study, we designed and implemented an electronic coaching (eCoaching) prototype system and deployed the same in the developed infrastructure to record and share personal health data in a secure way. Furthermore, we compared the test results with related studies qualitatively for the efficacy evaluation of the implemented security solution. The SFTSDH implementation and configuration in the prototype system has effectively secured the eCoach APIs from an attack in all the considered scenarios with 100% precision. The developed digital health infrastructure with SFTSDH solution sustained a load of (≈) 1000 concurrent users effectively. In addition, we performed a qualitative comparison among the following security solutions: SF security, third-party security, and SFTSDH, where SFTSDH showed a promising outcome.
— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.
Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a highsecurity guarantee and high efficiency. The proposed digital system consists of four different entities, user, data owner, cloud server, and key generation center (KGC). This approach makes the system more robust and highly secure, which has been verified with multiple scenarios. Besides, the proposed model consisted of the setup phase, key generation phase, encryption phase, validation phase, access control phase, and data sharing phase. The setup phases are run by the data owner, which takes input as a security parameter and generates the system master key and security parameter. Then, in the key generation phase, the private key is generated by KGC and is stored in the cloud server. After that, the generated private key is encrypted. Then, the session key is generated by KGC and granted to the user and cloud server for storing, and then, the results are verified in the validation phase using validation messages. Finally, the data is shared with the user and decrypted at the user-end. The proposed model outperforms other methods with a maximal genuine data rate of 0.91.
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