Big Data Security in Healthcare: Survey on Frameworks and Algorithms

Chandra, Sudipta; Ray, Soumya; Goswami, Radha Tamal

doi:10.1109/iacc.2017.0033

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…In a limited network resource, big data may overwhelm network capacity and effective WBAN performance. Hence increasing susceptibility to data anomalies and collision possibilities [41]. Anomalies are atypical characteristics observed in data or device operation showing discrepancy from the actual attributes.…”

Section: Data Anomalies and Network Faultsmentioning

confidence: 99%

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

2021

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This study presents a survey of the current issues, application areas, findings, and performance challenges in wireless body area networks (WBAN). The survey discusses selected areas in WBAN signal processing, network reliability, spectrum management, security, and WBAN integration with other technologies for highly efficient future healthcare applications. The foundation of the study bases on the recent growing advances in microelectronic technology and commercialization, which ease device availability, miniaturization, and communication. The survey considers a systemic review conducted using reports, standard documents, and peer-reviewed articles. Based on the comprehensive review, we find WBANs faces several operational, standardization, and security issues, affecting performance and maintenance of user safety and privacy. We envision the increasing dependency of future healthcare on WBAN for medical and non-medical applications due to internet connectivity advances. In this view, despite the WBAN advantages in remote health monitoring, further studies need to be conducted for performance optimization. Therefore we finalize our study by proposing various current and future research directions and open issues in WBAN’s performance enhancement.

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Section: Data Anomalies and Network Faultsmentioning

confidence: 99%

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

2021

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“…The common characteristic of all these applications is the utilization of massive data that are being generated in the health care sector to make better informed decisions. For instance, the collection of data generated by health care staff has been used for disease surveillance, decision support systems, detecting fraud, and enhancing privacy and security [29]. In fact, the code of conduct for the Norwegian health care sector requires the appropriate storage and protection of access logs of health care information systems for security reasons [3].…”

Section: Prior Studiesmentioning

confidence: 99%

Artificial Intelligence–Based Framework for Analyzing Health Care Staff Security Practice: Mapping Review and Simulation Study

Yeng¹,

Nweke²,

Yang³

et al. 2021

JMIR Med Inform

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Background Blocklisting malicious activities in health care is challenging in relation to access control in health care security practices due to the fear of preventing legitimate access for therapeutic reasons. Inadvertent prevention of legitimate access can contravene the availability trait of the confidentiality, integrity, and availability triad, and may result in worsening health conditions, leading to serious consequences, including deaths. Therefore, health care staff are often provided with a wide range of access such as a “breaking-the-glass” or “self-authorization” mechanism for emergency access. However, this broad access can undermine the confidentiality and integrity of sensitive health care data because breaking-the-glass can lead to vast unauthorized access, which could be problematic when determining illegitimate access in security practices. Objective A review was performed to pinpoint appropriate artificial intelligence (AI) methods and data sources that can be used for effective modeling and analysis of health care staff security practices. Based on knowledge obtained from the review, a framework was developed and implemented with simulated data to provide a comprehensive approach toward effective modeling and analyzing security practices of health care staff in real access logs. Methods The flow of our approach was a mapping review to provide AI methods, data sources and their attributes, along with other categories as input for framework development. To assess implementation of the framework, electronic health record (EHR) log data were simulated and analyzed, and the performance of various approaches in the framework was compared. Results Among the total 130 articles initially identified, 18 met the inclusion and exclusion criteria. A thorough assessment and analysis of the included articles revealed that K-nearest neighbor, Bayesian network, and decision tree (C4.5) algorithms were predominantly applied to EHR and network logs with varying input features of health care staff security practices. Based on the review results, a framework was developed and implemented with simulated logs. The decision tree obtained the best precision of 0.655, whereas the best recall was achieved by the support vector machine (SVM) algorithm at 0.977. However, the best F1-score was obtained by random forest at 0.775. In brief, three classifiers (random forest, decision tree, and SVM) in the two-class approach achieved the best precision of 0.998. Conclusions The security practices of health care staff can be effectively analyzed using a two-class approach to detect malicious and nonmalicious security practices. Based on our comparative study, the algorithms that can effectively be used in related studies include random forest, decision tree, and SVM. Deviations of security practices from required health care staff’s security behavior in the big data context can be analyzed with real access logs to define appropriate incentives for improving conscious care security practice.

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“…The common characteristics of all these applications is the utilization of massive data that is being generated in the healthcare sector to make better informed decisions. For instance, the collection of healthcare staffs' generated data, has been used for disease surveillance, decision support systems, detecting fraud and enhancing privacy and security [23]. In fact, the code of conduct for healthcare sector of Norway require the appropriate storage and protection of access logs of healthcare information systems for security reasons [24].…”

Section: Data-driven and Artificial Intelligence In Healthcare Security Practice Analysismentioning

confidence: 99%

Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Yeng

Nweke

Woldaregay

et al. 2020

Advances in Intelligent Systems and Computing

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Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the "human firewall," which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs' security practices in various scenarios including big data. The intention is to determine the gap between staffs' security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs' security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies.

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Big Data Security in Healthcare: Survey on Frameworks and Algorithms

Cited by 14 publications

References 19 publications

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

Artificial Intelligence–Based Framework for Analyzing Health Care Staff Security Practice: Mapping Review and Simulation Study

Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

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