Secure and Privacy-Aware Intelligent Healthcare Systems: A Review

Vijay, J. Antony; Kumar, C. D. Prem; Gomathi, B.

doi:10.1007/978-981-16-8150-9_10

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Over time, health care providers have learned the importance of routine system security updates and their potential to reduce malpractices such as identity theft, data breaches, and cyberattacks. 12 They have also learned how patient-controlled EHRs can encourage patient engagement, opt-in, active participation, and transparency in health care service delivery. Active bench-learning from other industries, such as banking, 13 retail, 14 and insurance, 15 have enabled health care providers to benefit from best practices and lessons learned on applying emerging technologies to achieve secure and effective data sharing.…”

Section: Health Care Providersmentioning

confidence: 99%

Personalized Health Care in a Data-Driven Era: A Post–COVID-19 Retrospective

Zahid

Sharma

2023

Mayo Clinic Proceedings: Digital Health

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Section: Health Care Providersmentioning

confidence: 99%

Personalized Health Care in a Data-Driven Era: A Post–COVID-19 Retrospective

Zahid

Sharma

2023

Mayo Clinic Proceedings: Digital Health

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“…The heart, the brain, chronic renal disease, etc., are the primary organs of focus. Sleep apnea, adverse medication reactions, chronic renal disease, and other conditions have all been linked to elevated blood pressure [18]. Here, a patient's excessive blood pressure may be validated by watching their ECG, blood pressure, glucose monitoring, oxygen monitoring, perspiration, electroencephalogram (EEG), and sleep monitoring data.…”

Section: Involvement Of Multiple Body Systemsmentioning

confidence: 99%

HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning

et al. 2022

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Utilization of the Internet of Things and ubiquitous computing in medical apparatuses have “smartified” the current healthcare system. These days, healthcare is used for more than simply curing patients. A Smart Healthcare System (SHS) is a network of implanted medical devices and wearables that monitors patients in real-time to detect and avert potentially fatal illnesses. With its expanding capabilities comes a slew of security threats, and there are many ways in which a SHS might be exploited by malicious actors. These include, but are not limited to, interfering with regular SHS functioning, inserting bogus data to modify vital signs, and meddling with medical devices. This study presents HealthGuard, an innovative security architecture for SHSs that uses machine learning to identify potentially harmful actions taken by users. HealthGuard monitors the vitals of many SHS-connected devices and compares the vitals to distinguish normal from abnormal activity. For the purpose of locating potentially dangerous actions inside a SHS, HealthGuard employs four distinct machine learning-based detection approaches (Artificial Neural Network, Decision Tree, Random Forest, and k-Nearest Neighbor). Eight different smart medical devices were used to train HealthGuard for a total of twelve harmless occurrences, seven of which are common user activities and five of which are disease-related occurrences. HealthGuard was also tested for its ability to defend against three distinct forms of harmful attack. Our comprehensive analysis demonstrates that HealthGuard is a reliable security architecture for SHSs, with a 91% success rate and in F1-score of 90% success.

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Secure and Privacy-Aware Intelligent Healthcare Systems: A Review

Cited by 2 publications

References 28 publications

Personalized Health Care in a Data-Driven Era: A Post–COVID-19 Retrospective

Personalized Health Care in a Data-Driven Era: A Post–COVID-19 Retrospective

HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning

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