Security and Privacy for mHealth and uHealth Systems: A Systematic Mapping Study

Abstract

An increased adoption of mobile health (mHealth) and ubiquitous health (uHealth) systems empower users with handheld devices and embedded sensors for a broad range of healthcare services. However, m/uHealth systems face significant challenges related to data security and privacy that must be addressed to increase the pervasiveness of such systems. This study aims to systematically identify, classify, compare, and evaluate state-of-the-art on security and privacy of m/uHealth systems. We conducted a systematic mapping study (SMS) based on 365 qualitatively selected studies to (i) classify the types, frequency, and demography of published research, (ii) synthesize and categorize research themes, (iii) recurring challenges, (iv) prominent solutions (i.e., research outcomes) and their (v) reported evaluations (i.e., practical validations). Results suggest that the existing research on security and privacy of m/uHealth systems primarily focuses on select group of control families (compliant with NIST800-53), protection of systems and information, access control, authentication, individual participation, and privacy authorisation. In contrast, areas of data governance, security and privacy policies, and program management are underrepresented, although these are critical to most of the organizations that employ m/uHealth systems. Most research proposes new solutions with limited validation, reflecting a lack of evaluation of security and privacy of m/uHealth in the real world. Empirical research, development, and validation of m/uHealth security and privacy is still incipient, which may discourage practitioners from readily adopting solutions from the literature. This SMS facilitates knowledge transfer, enabling researchers and practitioners to engineer security and privacy for emerging and next generation of m/uHealth systems.

Appsolutely secure? Psychometric properties of the German version of an app information privacy concerns measure during COVID-19

“…The context of COVID-19 contact tracing apps provides an important yet specific scenario to study app information privacy concerns: As pointed out above, health concerns might play an important role in determining privacy concerns, which might not be the case in other use cases (e.g., online banking, e-commerce). This aspect is both a strength and a weakness of the study, because it provides a connection to mHealth research (e.g., Iwaya et al, 2020;Gupta et al, 2021;Hensher et al, 2021), yet it also leads to questions about the validity of these findings for different domains, and potential, health-related confounders Tomczyk 10.3389/fpsyg.2022.899092 (Hew et al, 2015). The study reported findings for different configurations of AIPC (three-and four-factor models).…”

“…So far, many studies have analyzed privacy policies of app providers, reviewed or suggested measures or content of data security statements and policies. However, real-world evaluations of these suggestions are scarce (e.g., Li, 2011;Sunyaev et al, 2015;Chib and Lin, 2018;Benjumea et al, 2020;Iwaya et al, 2020;Grundy, 2022). Consequently, studies should focus on user perceptions of information privacy.…”

“…In fact, many apps gather (sensitive) personal data, and the more data, the more precise the customization to individual needs, which supports successful treatment processes. Hence, highly customizable apps require a trade-off between information privacy and comfort or customization (Jeminiwa et al, 2019;Iwaya et al, 2020;Hensher et al, 2021;Grundy, 2022). Information privacy refers to the degree of autonomous and self-directed disclosure of private information (Smith et al, 2011).…”

Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives

“…For instance, by empowering communication between developers and HCPs in defining safety priorities, patients will also benefit in trust when using the mobile app. Hence, instead of a technical implementation, data security becomes a key factor within empowering trust and safety of involved parties [ 64 ].…”

Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

“…In health based mobile app recommendation especially for the areas of nutrition and physical activity, there are few schemes are available as discussed in recent literature review [49]. In the same manner, a recent literature review [50] clearly mentioned that security and privacy issues are still under-explored in mobile health applications. So, RDT algorithm would be a nice addition to the said domain.…”