Security Solutions for Big Data Analytics in Healthcare

Rao, S. V. Kasi Visweswara; Suma, Sugimiyanto; Sunitha, M.

doi:10.1109/icacce.2015.83

Cited by 16 publications

(9 citation statements)

References 3 publications

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“…The technology presents capabilities to enhance managing chronic diseases and monitoring daily fitness goals [72]. Within smart healthcare systems, IoT infrastructures can help to bridge the gap between patient and their doctors [73]. The infrastructures can collect and transmit crucial medical information from patients [74].…”

Section: Internet Of Things (Iot) In Smart Healthmentioning

confidence: 99%

Impact of Big Data Analysis on Health

Rosário¹,

Dias²

2022

Preprint

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Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

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Section: Internet Of Things (Iot) In Smart Healthmentioning

confidence: 99%

Impact of Big Data Analysis on Health

Rosário¹,

Dias²

2022

Preprint

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“…In healthcare, for instance, the security issues of information extraction from massive amount of data and accurate analytics are of high importance. Sensitive data recorded in databases need to be protected via monitoring which applications and users get accesses to the data [204]. In order to guarantee a strong secure big data analytics, the following tasks can be performed [205]:…”

Section: B Security In Big Data Analyticsmentioning

confidence: 99%

“…The work [270] adopted an energy-efficient architecture for Industrial Internet of Things (IIoT) with a sense entities domain, RESTful service hosted networks, a cloud server, and user applications to balance the traffic load and support a longer lifetime of the whole system. The paper [271] mainly studied two important issues: 1) performing cyber-physical systems (CPS) communications over cellular [196] Secure data placement on cloud i) ensures users' data protection on clouds; ii) does not require complex signatures and keys; iii) provides data privacy at users' side i) data might need to be encrypted/decrypted before transferring to clouds; ii) can have a long data retrieval time; iii) not suitable for making real-time network decisions; iv) not very robust as attackers can guess data's locations; v) service providers can have access to users' data; vi) complicated data management and access [195] [197] [199] [202] [223] [215] Cryptographic Solutions i) ensures information protection, data privacy and integrity; ii) works in conjunction with other security solutions for more robust security i) costly in terms of computational overhead; ii) can have a large power consumption; iii) increases latency; iv) inefficient for many CPS devices with limited computational capabilities; v) works only for fixed malicious behaviors Data Access Permission Restrictions [50] [204] [224] [225] Advanced security controls i) selective access control; ii) can be used in conjunction with cryptography for a more robust security; iii) good for real-time data streams; iv) provides additional layer of protection i) difficult to access for legitimate users; ii) needs to be combined with other solutions; iii) not sufficient by itself to protect users' data; iv) might increase latency from accessing the data Visualization Detection of Anomalies [130] Anomaly Detection i) does not require costly solutions such as signatures and keys; ii) works for a variety of attacks with varying malicious behaviors; iii) can detect abnormal traffic patterns with no associated signature i) requires a large dataset for training; ii) costly in terms of overhead and storage of training data; iii) might require human analysts' intervention to make the detection more textbackslash supervise [179] [231] [130] [209] [211] [232] Visualization Techniques i) ideal for monitoring real-time traffic; ii) provides global overview of network problems i) not accurate if traffic patterns are misclassified; ii) requires complex statistical analysis of traffic; iii) might need to be supervised by humans; iv) might require prior knowledge for a better intrusion detection -Provide relay incentives to extend communication [24], [56], [128], [145], [233]...…”

Section: A Greening Big Data For Cps 1) Green Data Collections and Co...mentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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“…The use of latest and emerging technologies, sensors and sharing techniques, and information and communication techniques make it easier to generate and share healthcare data but also introduces challenges with respect to security and privacy. This is because electronic healthcare data is susceptible to unlawful access, compromise integrity, and unauthorized distribution [53,54,55].…”

Section: Security and Privacy Challenges In Healthcarementioning

confidence: 99%

“…This is because electronic healthcare data is susceptible to unlawful access, compromise integrity, and unauthorized distribution. [53][54][55] Another major challenge with big data applications in the healthcare sector is complex and distributed nature of healthcare data, diverse schema and standards, and rapid growth of new health terminologies and ontologies. The major challenge here is not the lack of data but the lack of information to support decision making, planning, and strategy.…”

Section: Security and Privacy Challenges In Healthcarementioning

confidence: 99%

A privacy‐preserving framework for smart context‐aware healthcare applications

Azad

Arshad

Mahmoud

et al. 2019

Trans Emerging Tel Tech

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Internet of things (IoT) is a disruptive paradigm with wide ranging applications including healthcare, manufacturing, transportation and retail. Within healthcare, smart connected wearable devices are widely used to achieve improved wellbeing, quality of life and security of citizens. Such connected devices generate significant amount of data containing sensitive information about patient requiring adequate protection and privacy assurance. Unauthorized access to an individual's private data constitutes a breach of privacy leading to catastrophic outcomes for an individuals personal and professional life. Furthermore, breach of privacy may also lead to financial loss to the governing body such as those proposed as part of the General Data Protection Regulation (GDPR) in Europe. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability and pervasive processing, it also introduces challenges with respect to scalability, reliability and context-awareness for its applications. This paper is focused on privacy preservation within smart context-aware healthcare with a special emphasis on privacy assurance challenges within the Electronic Transfer of Prescription (ETP). To this extent, we present a case for a comprehensive, coherent, and dynamic privacypreserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models we propose an enhancement for the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for cutting edge IoT applications such as smart healthcare whilst addressing unique challenges with respect to context-aware mobility of such applications.

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Security Solutions for Big Data Analytics in Healthcare

Cited by 16 publications

References 3 publications

Impact of Big Data Analysis on Health

Impact of Big Data Analysis on Health

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

A privacy‐preserving framework for smart context‐aware healthcare applications

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