Privacy Preserving and Security Management in Cloud-Based Electronic Health Records—A Survey

Prathima, S.; Priya, C.

doi:10.1007/978-981-15-3284-9_3

Cited by 3 publications

(5 citation statements)

References 28 publications

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“…Different techniques and methods have been practiced and implemented by the experts and the researchers to secure the electronic health record processing. Both cryptographic, as well as non-cryptographic techniques, have been applied in different environments to achieve confidentiality and privacy in healthcare data [11,12]. But there are very few studies that implement machine learning approaches to improve the confidentiality and privacy of healthcare data [13].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Seh¹,

Al-Amri²,

Subahi³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients' data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital services. However, there are very few from the Machine learning (ML) domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records (EHRs). The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches. B.A. Kitchenham guidelines have been practiced as a manual to conduct this work. Seven well-known digital libraries namely IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, Willey Online Library, PubMed (Medical and Bio-Science), and MDPI have been included to perform an exhaustive search for the existing pertinent studies. Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion, yet the specified area has not been fully explored by the researchers. K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems' log data. Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies. This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively. Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

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Section: Introductionmentioning

confidence: 99%

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Seh¹,

Al-Amri²,

Subahi³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Currently, systems at various clinics are fragmented and disconnected from each other [ 34 ]. At some clinics, the exchange of patients’ data between dental care providers is made through a manufacturer portal based on the local or global server storage, hence the dental company has access and control of the data [ 35 , 36 ]. This approach of data exchange is limited to homogeneous organizations that operate the same EDR software system, and this approach is not interoperable between various dental care organizations [ 37 , 38 ].…”

Section: Resultsmentioning

confidence: 99%

“…Figure 3 represents the general architecture for existing EDR systems where the data stored on the central or local storage. Clinics that have the same vendor software and access rights can retrieve from and upload the dental data into global storage [ 36 , 41 , 42 ]. However, in case of the local storage architecture, each clinic has its own storage which cannot be directly shared with other dental providers.…”

Section: Resultsmentioning

confidence: 99%

“… Journal 2012 [ 48 ] Blockchain and smart contracts to improve dental healthcare for children in primary school Conference 2021 [ 34 ] Patient-Centric Authorization Framework for Sharing Electronic Health Records, Conference 2009 [ 50 ] Blockchain in healthcare: smart contracts to improve dental healthcare for children in mixed dentition period Thesis 2021 [ 35 ] A novel digital dentistry platform based on cloud manufacturing paradigm Journal 2019 [ 51 ] Towards a unified blockchain-based dental record ecosystem for disaster victims’ identification. Conference 2019 [ 36 ] Privacy Preserving and Security Management in Cloud-Based Electronic Health Records Conference 2020 [ 52 ] A decentralised blockchain dentistry during and after the COVID 19 crisis N/A 2022 [ 37 ] The virtual dental home: implications for policy and strategy. Journal 2012 [ …”

Section: Table A1mentioning

confidence: 99%

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The Potential of Blockchain Technology in Dental Healthcare: A Literature Review

Mokhamed

Talib

Moufti

et al. 2023

Sensors

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Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues.

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“…In this case, the third party (trusted one) could manage the client's files on his behalf and allow clients to monitor [27]. A Blacklisted users authorized and trusted list of users, intrusion detection notifications could be utilized to enhance privacy [14,28].…”

Section: Sticky Policy and Blockchainmentioning

confidence: 99%

PDIS: A Service Layer for Privacy and Detecting Intrusions in Cloud Computing

Mumtaz¹,

Samawi²,

Alhroob³

et al. 2022

ijasca

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Cloud computing faces numerous challenges in many areas including security and privacy issues. In this work, a developed approach is suggested to tackle three security and privacy issues: network intrusion detection (NID), privacy, and internal attacks. A decision tree (J48) has been used to generate a set of rules based on the CICIDS2017 dataset to solve the NID problem. The accuracy of the generated rules approaches 99.8%. A set of policies are attached to the data file on the bases of a sticky policy to preserve privacy. A new approach is suggested based on blockchain to detect internal attacks in real-time, in which a set of trustees-chain are identified by the data owner. Any data modification conducted by a trusted member will be reported to all members of the trust group including the owner. The developed approach suggests adding a Privacy and Detecting Intrusions Service (PDIS) layer as part of the cloud computing main service model. PDIS includes the three suggested approaches above (NID, sticky policy, and trustees-chain). Finally, a web-based application is implemented to act as casework to validate PDIS and evaluate its reliability. Keywords: Cloud computing, Privacy, Machine learning, Internal Intrusion Detection, Network Intrusion Detection, Real Time Auditing

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Privacy Preserving and Security Management in Cloud-Based Electronic Health Records—A Survey

Cited by 3 publications

References 28 publications

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

The Potential of Blockchain Technology in Dental Healthcare: A Literature Review

PDIS: A Service Layer for Privacy and Detecting Intrusions in Cloud Computing

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