Real-time Secure Health Surveillance for Smarter Health Communities

Alabdulatif, Abdulatif; Khalil, Ibrahim; Forkan, Abdur Rahim Mohammad; Atiquzzaman, Mohammed

doi:10.1109/mcom.2017.1700547

Cited by 43 publications

(33 citation statements)

References 14 publications

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“…Alabdulatif et al implement a system that provides a privacy-preserving cloud-based real-time change detection and abnormality prediction framework for multiple vital signs of a patient in [13]. The system is composed of three main blocks; the Smart Community Resident, where data is collected and aggregated to be sent to the Cloud Storage, where data is stored in an encrypted format.…”

Section: Related Workmentioning

confidence: 99%

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Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

et al. 2020

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Introducing IoT systems to healthcare applications has made it possible to remotely monitor patients' information and provide proper diagnostics whenever needed. However, providing high-security features that guarantee the correctness and confidentiality of patients' data is a significant challenge. Any alteration to the data could affect the patients' treatment, leading to human casualties in emergency conditions. Due to the high dimensionality and prominent dynamicity of the data involved in such systems, machine learning has the promise to provide an effective solution when it comes to intrusion detection. However, most of the available healthcare intrusion detection systems either use network flow metrics or patients' biometric data to build their datasets. This paper aims to show that combining both network and biometric metrics as features performs better than using only one of the two types of features. We have built a real-time Enhanced Healthcare Monitoring System (EHMS) testbed that monitors the patients' biometrics and collects network flow metrics. The monitored data is sent to a remote server for further diagnostic and treatment decisions. Man-in-the-middle cyber-attacks have been used, and a dataset of more than 16 thousand records of normal and attack healthcare data has been created. The system then applies different machine learning methods for training and testing the dataset against these attacks. Results prove that the performance has improved by 7% to 25% in some cases, and this shows the robustness of the proposed system in providing proper intrusion detection.

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Section: Related Workmentioning

confidence: 99%

“…A secret cipher algorithm is implemented and optimized on the FPGA hardware platform for data collection with security. This approach has the complications of hardware approaches in addition to the problems in [13].…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

et al. 2020

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“…However, Lopez et al [ 15 ] have observed that the study in [ 14 ] has not considered the privacy risk. Alabdulatif et al [ 16 ] have explored the aim of creating a health surveillance system, making health groups smarter with the goal of detecting frequent variations in the patient's signs, elders living in assisted living surroundings, and healthy people living in smart home. The patient's data privacy problem has been handled with cloud computing by sharing the reading of vital signs to medical staff via the protected IoT environment.…”

Section: Related Workmentioning

confidence: 99%

Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network

Ullah

Khan

et al. 2020

Journal of Healthcare Engineering

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There is a need to develop an effective data preservation scheme with minimal information loss when the patient’s data are shared in public interest for different research activities. Prior studies have devised different approaches for data preservation in healthcare domains; however, there is still room for improvement in the design of an elegant data preservation approach. With that motivation behind, this study has proposed a medical healthcare-IoTs-based infrastructure with restricted access. The infrastructure comprises two algorithms. The first algorithm protects the sensitivity information of a patient with quantifying minimum information loss during the anonymization process. The algorithm has also designed the access polices comprising the public access, doctor access, and the nurse access, to access the sensitivity information of a patient based on the clustering concept. The second suggested algorithm is K-anonymity privacy preservation based on local coding, which is based on cell suppression. This algorithm utilizes a mapping method to classify the data into different regions in such a manner that the data of the same group are placed in the same region. The benefit of using local coding is to restrict third-party users, such as doctors and nurses, when trying to insert incorrect values in order to access real patient data. Efficiency of the proposed algorithm is evaluated against the state-of-the-art algorithm by performing extensive simulations. Simulation results demonstrate benefits of the proposed algorithms in terms of efficient cluster formation in minimum time, minimum information loss, and execution time for data dissemination.

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“…Researchers have paved a context‐aware algorithm to estimate the bodily motion of a human 51 . Secure, privacy‐aware, and real‐time health monitoring and surveillance mechanism were also tested against an IoT‐based community centric aspect 52,53 . Thermal comfort is another issue that must be successfully provided to the inhabitant for his/her well stay.…”

Section: Related Workmentioning

confidence: 99%

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

Ray

Dash

Moustafa

2020

Trans Emerging Tel Tech

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Bio‐sensor data streaming and analytics is a key component of smart e‐healthcare. However, existing Internet of Things (IoT) ecosystem is unable to materialize the real‐time bio‐sensor data streaming and analytics within resource constrained environment. Moreover, traditional solutions fail to mitigate the edge‐cloud integration within a single sub‐system under IoT periphery which lead to investigate how edge‐cloud hybridization could be realized via similar set of tools. The objective of this article is to implement an integrated dual‐mode edge‐cloud system to serve streaming and analytics in real‐time. This study aims to achieve the aforesaid goal by presenting two different experiments that deals with the real‐time pulse sensor data streaming and analytics while utilizing light‐weight IoT‐supported JavaScript frameworks that includes Node.js, Johnny‐Five, Serialport.js, Plotly client, Flot.js, jQUERYy, Express Server, and Socket.io. Firstly, a standalone IoT‐edge system is developed and later, an integrated IoT‐based edge‐cloud system is developed to compare between the effectiveness of the systems. The implementation results show near correlation between the standalone edge and dual‐mode edge system. However, the dual‐mode edge‐cloud system provides more flexibility and capability to counter the bio‐sensor data streaming and analytics services within the constrained framework.

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Real-time Secure Health Surveillance for Smarter Health Communities

Cited by 43 publications

References 14 publications

Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

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