Machine learning cryptography methods for IoT in healthcare.

BACKGROUND: The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices. METHODS: This study evaluates the performance of eight LWC algorithms-AES, PRESENT, MSEA, LEA, XTEA, SIMON, PRINCE, and RECTANGLE-using machine learning models. Experiments were conducted on a Raspberry Pi 3 microcontroller using 16 KB to 2048 KB files. Machine learning models were trained and tested for each LWC algorithm and their performance was evaluated based using precision, recall, F1-score, and accuracy metrics. RESULTS: The study analyzed the encryption/decryption execution time, energy consumption, memory usage, and throughput of eight LWC algorithms. The RECTANGLE algorithm was identified as the most suitable and efficient LWC algorithm for IoT in healthcare due to its speed, efficiency, simplicity, and flexibility. CONCLUSIONS: This research addresses security and privacy concerns in IoT healthcare and identifies key performance factors of LWC algorithms utilizing the SLR research methodology. Furthermore, the study provides insights into the optimal choice of LWC algorithm for enhancing privacy and security in IoT healthcare environments.

Individuals' privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective.

OBJECTIVE: The study sought to develop and empirically validate an integrative situational privacy calculus model for explaining potential users' privacy concerns and intention to install a contact tracing mobile application (CTMA). MATERIALS AND METHODS: A survey instrument was developed based on the extant literature in 2 research streams of technology adoption and privacy calculus. Survey participants (N = 853) were recruited from all 50 U.S. states. Partial least squares structural equation modeling was used to validate and test the model. RESULTS: Individuals' intention to install a CTMA is influenced by their risk beliefs, perceived individual and societal benefits to public health, privacy concerns, privacy protection initiatives (legal and technical protection), and technology features (anonymity and use of less sensitive data). We found only indirect relationships between trust in public health authorities and intention to install CTMA. Sex, education, media exposure, and past invasion of privacy did not have a significant relationship either, but interestingly, older people were slightly more inclined than younger people to install a CTMA. DISCUSSION: Our survey results confirm the initial concerns about the potentially low adoption rates of CTMA. Our model provides public health agencies with a validated list of factors influencing individuals' privacy concerns and beliefs, enabling them to systematically take actions to address these identified issues, and increase CTMA adoption. CONCLUSIONS: Developing CTMAs and increasing their adoption is an ongoing challenge for public health systems and policymakers. This research provides an evidence-based and situation-specific model for a better understanding of this theoretically and pragmatically important phenomenon.

Health Data Privacy: Research Fronts, Hot Topics and Future Directions.

Health data privacy is an important research stream due to the high impacts on the success of digital health transformation and implementation. Neglecting to safeguard data confidentially and integrity and mitigate risks associated with unauthorized access will lead to failures in materializing benefit from digital health. This study aims to present a bibliometric analysis of health data privacy and provide a platform for future directions. We conducted a literature search between 2010 and 2020 in the Web of Science (WoS) database, resulted in 1,752 records. As part of the bibliometric analysis, concept mapping of health data privacy researches was depicted by network visualization and overlay visualization. These two visualizations represent five research fronts and emerging topics (e.g., digital health, blockchain, the internet of things (IoT)). Finally, we chart directions for future research on health data privacy, highlighting emerging topics, and boundary-breaking alternatives (e.g., GDPR, contact tracing apps in the context of pandemics).

General Data Protection Regulation (GDPR) in Healthcare: Hot Topics and Research Fronts.

General Data Protection Regulation came into effect across the European Union in May 2018 but its implications in healthcare are yet to be fully understood. The aim of this study was to identify the fronts and hot topics in research on GDPR in healthcare. We analyzed the relevant records in Scopus through bibliometric and scientometric approach and visualization techniques. A set of 155 records was obtained and processed for co-occurrence analysis of key terms and concept mapping. The number of published papers showed a steep rise in the past two years, mainly by European countries. Analysis of the abstract of the papers showed that data protection, privacy, and big data were the most frequently used terms. Three dominant research fronts of GDPR are 1) general implications of GDPR, 2) technology aspects of GDPR, and 3) GDPR in healthcare service. Blockchain and machine learning are among the remerging topics of GDPR research.