Certificateless Algorithm for Body Sensor Network and Remote Medical Server Units Authentication over Public Wireless Channels

Wireless sensor networks process and exchange mission-critical data relating to patients’ health status. Obviously, any leakages of the sensed data can have serious consequences which can endanger the lives of patients. As such, there is need for strong security and privacy protection of the data in storage as well as the data in transit. Over the recent past, researchers have developed numerous security protocols based on digital signatures, advanced encryption standard, digital certificates and elliptic curve cryptography among other approaches. However, previous studies have shown the existence of many security and privacy gaps that can be exploited by attackers to cause some harm in these networks. In addition, some techniques such as digital certificates have high storage and computation complexities occasioned by certificate and public key management issues. In this paper, a certificateless algorithm is developed for authenticating the body sensors and remote medical server units. Security analysis has shown that it offers data privacy, secure session key agreement, untraceability and anonymity. It can also withstand typical wireless sensor networks attacks such as impersonation, packet replay and man-in-the-middle. On the other hand, it is demonstrated to have the least execution time and bandwidth requirements

Security for networked smart healthcare systems: A systematic review

Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry

E-SAP: Efficient-Strong Authentication Protocol for Healthcare Applications Using Wireless Medical Sensor Networks

A wireless medical sensor network (WMSN) can sense humans’ physiological signs without sacrificing patient comfort and transmit patient vital signs to health professionals’ hand-held devices. The patient physiological data are highly sensitive and WMSNs are extremely vulnerable to many attacks. Therefore, it must be ensured that patients’ medical signs are not exposed to unauthorized users. Consequently, strong user authentication is the main concern for the success and large scale deployment of WMSNs. In this regard, this paper presents an efficient, strong authentication protocol, named E-SAP, for healthcare application using WMSNs. The proposed E-SAP includes: (1) a two-factor (i.e., password and smartcard) professional authentication; (2) mutual authentication between the professional and the medical sensor; (3) symmetric encryption/decryption for providing message confidentiality; (4) establishment of a secure session key at the end of authentication; and (5) professionals can change their password. Further, the proposed protocol requires three message exchanges between the professional, medical sensor node and gateway node, and achieves efficiency (i.e., low computation and communication cost). Through the formal analysis, security analysis and performance analysis, we demonstrate that E-SAP is more secure against many practical attacks, and allows a tradeoff between the security and the performance cost for healthcare application using WMSNs

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

Efficient data uncertainty management for health industrial internet of things using machine learning

[EN] In modern technologies, the industrial internet of things (IIoT) has gained rapid growth in the fields of medical, transportation, and engineering. It consists of a self-governing configuration and cooperated with sensors to collect, process, and analyze the processes of a real-time system. In the medical system, healthcare IIoT (HIIoT) provides analytics of a huge amount of data and offers low-cost storage systems with the collaboration of cloud systems for the monitoring of patient information. However, it faces certain connectivity, nodes failure, and rapid data delivery challenges in the development of e-health systems. Therefore, to address such concerns, this paper presents an efficient data uncertainty management model for HIIoT using machine learning (EDM-ML) with declining nodes prone and data irregularity. Its aim is to increase the efficacy for the collection and processing of real-time data along with smart functionality against anonymous nodes. It developed an algorithm for improving the health services against disruption of network status and overheads. Also, the multi-objective function decreases the uncertainty in the management of medical data. Furthermore, it expects the routing decisions using a machine learning-based algorithm and increases the uniformity in health operations by balancing the network resources and trust distribution. Finally, it deals with a security algorithm and established control methods to protect the distributed data in the exposed health industry. Extensive simulations are performed, and their results reveal the significant performance of the proposed model in the context of uncertainty and intelligence than benchmark algorithms.This research is supported by Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh Saudi Arabia. Authors are thankful for the support.Haseeb, K.; Saba, T.; Rehman, A.; Ahmed, I.; Lloret, J. (2021). Efficient data uncertainty management for health industrial internet of things using machine learning. International Journal of Communication Systems. 34(16):1-14. https://doi.org/10.1002/dac.4948114341