Real-Time Medical Systems Based on Human Biometric Steganography: a Systematic Review

Mohsin, A. H.; Zaidan, A. A.; Zaidan, B. B.; Ariffin, Shamsul Arrieya; Albahri, O. S.; Albahri, A. S.; Alsalem, M. A.; Mohammed, K. I.; Hashim, Mohammad

doi:10.1007/s10916-018-1103-6

Cited by 50 publications

(18 citation statements)

References 79 publications

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“…Privacy challenges also arise with adoption of ML/DL techniques for building biometric healthcare systems either offline (e.g., face or fingerprint recognition based system to protect medical facilities and equipment [110]) or online systems, e.g., real-time medical systems [111] and use of biometrics for authentication of medical IoT devices [112], etc. The security and privacy of such systems are of utmost importance; therefore, worst-case robustness test should be performed for biometrically secure healthcare systems.…”

Section: ML For Healthcare: Challengesmentioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

346

144

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Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from onedimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

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Section: ML For Healthcare: Challengesmentioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

346

144

View full text Add to dashboard Cite

show abstract

“…A sufficient number of studies on FV biometric technology covering a wide scope of disciplines can be obtained from these databases ( Figure 2). During the retrieval process, search, selection and categorisation of relevant studies from these databases were carried out in three screening and filtering iteration processes [17]- [20]. In the first iteration process, duplicate articles were eliminated [21]- [24].…”

Section: Methodsmentioning

confidence: 99%

“…The attacker can transfer these unknown data to another system or location and attempt to break the decryption of cipher text by using modern programmes and techniques [108]- [112]. Thus, the confidentiality of such information must be enhanced by using data hiding techniques, such as steganography or watermarking [17], [113]- [147]. The requirements needed to solve this problem according to the definition of information security by the international standard ISO/IEC 27002 (2005) should also be improved.…”

Section: New Proposed Solutionmentioning

confidence: 99%

Finger Vein Biometrics: Taxonomy Analysis, Open Challenges, Future Directions, and Recommended Solution for Decentralised Network Architectures

Mohsin¹,

Jalood²,

Alamoodi

et al. 2020

IEEE Access

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A review is conducted to deeply analyse and map the research landscape of current technologies in finger vein (FV) biometric authentication in medical systems into a coherent taxonomy. This research focuses on articles related to the keywords 'biometrics', 'finger veins' and 'verification' and their variations in three major databases, namely, Web of Science, ScienceDirect and IEEE Xplore. The final set of collected articles related to FV biometric authentication systems is divided into software-and hardware-based systems. In the first category, software development attempts are described. The experiment results, frameworks, algorithms and methods that perform satisfactorily are presented. Moreover, the experiences obtained from conducting these studies are discussed. In the second category, hardware development attempts are described. The final articles are discussed from three aspects, namely, (1) number of publications, (2) problem type, proposed solutions, best results and evaluation methods in the included studies and (3) available databases containing different scientific work collected from volunteers, such as staff and students. The basic characteristics of this emerging field are identified from the following aspects: motivations of using FV biometric technology in authentication systems, open challenges that impede the technology's utility, authors' recommendations and future research prospects. A new solution is proposed to address several issues, such as leakage of biometrics that leads to serious risks due to the use of stolen FV templates and various spoofing and brute-force attacks in decentralised network architectures in medical systems, including access points and various database nodes without a central point. This work contributes to literature by providing a detailed review of feasible alternatives and research gaps, thereby enabling researchers and developers to develop FV biometric authentication medical systems further. Insights into the importance of such a technology and its integration into different medical applications and fields are also provided.

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“…Telemedicine has been increasingly used in healthcare because it offers several benefits, such as providing the healthrelated information of patients [1][2][3][4][5][6][7] and remote healthcare services [8][9][10][11][12][13][14]. Telemedicine is a remote medical practice in which different healthcare providers cooperate and permit their collaborative efforts in the diagnosis or treatment of a disease [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

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Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decisionmaking theory to address the current problems in the prioritisation of patients with MCDs.

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Real-Time Medical Systems Based on Human Biometric Steganography: a Systematic Review

Cited by 50 publications

References 79 publications

Secure and Robust Machine Learning for Healthcare: A Survey

Secure and Robust Machine Learning for Healthcare: A Survey

Finger Vein Biometrics: Taxonomy Analysis, Open Challenges, Future Directions, and Recommended Solution for Decentralised Network Architectures

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

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