Privacy and Security of IoT Based Healthcare Systems: Concerns, Solutions, and Recommendations

Abstract

Although emerging IoT paradigms in sleep tracking have a substantial contribution to enhancing current healthcare systems, there are several privacy and security considerations that end-users need to consider. End-users can be susceptible to malicious threats when they allow permission to potentially vulnerable or leaky third-party apps. Since the data is migrated to the cloud, it goes over insecure communication channels, all of which have their security concerns. Moreover, there are alternative data violation concerns when the data projects into the proprietor's cloud storage facility. In this study, we present some of the existing IoT sleep trackers, also we discuss the most common features associated with these sleep trackers. As the majority of end-users are not aware of the privacy and security concerns affiliated with emerging IoT sleep trackers. We review existing solutions that can apply to IoT sleep tracker architecture. Also, we describe a deployed IoT platform that can address these concerns. Finally, we provide some of the recommendations to end-users and service providers to ensure a safer approach while leveraging the IoT sleep tracker in caregiving. This incorporates recommendations for software updates, awareness programs, software installation, and social engineering.

Applications and Techniques for Fast Machine Learning in Science

“…A new trend in this field has started relying on implementing models that can be implemented on-device and are both quick and accurate in detecting these behaviors. In addition to providing real-time interventions (Thomas and Bond, 2015 ; Nahum-Shani et al, 2018 ), on-device monitoring of these behaviors can reduce privacy concerns (Sadek et al, 2019 ). However, since wearable devices themselves might not be capable of processing the data, federated machine learning approaches are also being explored recently by several researchers (Rieke et al, 2020 ).…”

Self-healing Approach for IoT Architecture: AMI Platform

“…Since data is collected through sensors, a lack of memory resources leads to data loss and overload of the processors block the gateways or cloud servers from operating in their optimal way and even disable them. 4) Human interaction issues: There is a high probability that human intervention can be part of a system failure [16] through: Implementation defects and operational mistakes, Co-programming and Formal Design. * a) Implementation defects and operational mistakes signify faults caused by human errors [8].…”

“…* a) Data Security regroups data stealing, data loss, and data privacy. The importance of self-awareness of issues compromising data has been documented [16]. Attackers are well versed in a data attack through techniques such as man-in-the-middle attack, phishing, intrusion to get the information they need.…”

An efficient image encryption scheme for healthcare applications

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