A Context-Aware Security Model for a Combination of Attribute-Based Access Control and Attribute-Based Encryption in the Healthcare Domain

Psarra, Evgenia; Verginadis, Yiannis; Patiniotakis, Ioannis; Apostolou, Dimitris; Mentzas, Gregoris

doi:10.1007/978-3-030-44038-1_104

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…Simple context conditions can be combined in order to form complex context conditions, using the standard AND, OR, NOT operators. A complete list of the contextual attributes, represented in a context model, is described in the work of Psarra et al [34].…”

Section: Criticality Assessment Using Fuzzy Context Handlersmentioning

confidence: 99%

“…The criticality assessment of a patient's condition is made using a rule-based approach. Note, that since many of the contextual variables appearing in [34] are evaluated by experts using linguistic terms, such as 'if blood pressure is high', our approach adopts the following fuzzy inferencing process:…”

Section: Criticality Assessment Using Fuzzy Context Handlersmentioning

confidence: 99%

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Accessing electronic health records in critical incidents using context-aware attribute-based access control

Psarra

Verginadis

Patiniotakis

et al. 2022

IDT

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In emergency situations, different actors involved in first aid services should be authorized to retrieve information from the patient’s Electronic Health Records (EHRs). The research objectives of this work involve the development and implementation of methods to characterise emergency situations requiring extraordinary access to healthcare data. The aim is to implement such methods based on contextual information pertaining to specific patients and emergency situations and also leveraging personalisation aspects which enable the efficient access control on sensitive data during emergencies. The Attribute Based Access Control paradigm is used in order to grant access to EHRs based on contextual information. We introduce an ABAC approach using personalized context handlers, in which raw contextual information can be uplifted in order to recognize critical situations and grant access to healthcare data. Results indicate that context-aware ABAC is a very effective method for detecting critical situations that require emergency access to personal health records. In comparison to RBAC implementations of emergency access control to EHRs, the proposed ABAC implementation leverages contextual information pertaining to the specific patient and emergency situations. Contextual information increases the capability of ABAC to recognize critical situations and grant access to healthcare data.

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Section: Criticality Assessment Using Fuzzy Context Handlersmentioning

confidence: 99%

Section: Criticality Assessment Using Fuzzy Context Handlersmentioning

confidence: 99%

Accessing electronic health records in critical incidents using context-aware attribute-based access control

Psarra

Verginadis

Patiniotakis

et al. 2022

IDT

Self Cite

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“…A fuzzy context handler uses fuzzy rules that associate contextual attributes with fuzzy values and generates as output an assessment of the criticality of the incident. The related contextual attributes, which are represented in a context model, are presented in detail in [61]. Here, we extend the fuzzy context handlers by The predictive mechanism, implemented with LSTM, receives as input the recent health metrics and outputs the predictions of health metrics for the next two hours.…”

Section: Fuzzy Context Handlersmentioning

confidence: 99%

Context-Based, Predictive Access Control to Electronic Health Records

et al. 2022

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Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

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“…To address the shortcomings of traditional electronic medical systems' inefficiency and scalability, certain schemes 15,16 use access control technologies to administer EHR stored in the cloud. These strategies can ensure the security of remotely stored data in cloud computing, demonstrating that giving data owners access control over their data is more vital than allowing the cloud to control data.…”

Section: Related Workmentioning

confidence: 99%

A fine‐grained and secure health data sharing scheme based on blockchain

Chen

Yin

Ning

2022

Trans Emerging Tel Tech

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Electronic health record (EHR) allows patients to use an open channel (ie, Internet) to control, share and manage their health records among family members, healthcare providers and other third party data users. Thus, in such an environment, privacy, confidentiality, and data consistency are the major challenges. Although cloud‐based EHR addresses the aforementioned discussions, these are prone to various malicious attacks, trust management and non‐repudiation among servers. Recently, due to the property of immutability, blockchain technology has been introduced to be as an auspicious solution for achieving EHR sharing with privacy and security preservation. Motivated by the above debates, we present BFHS, a blockchain‐based fine‐grained secure EHR sharing mechanism. On BFHS, we encrypt the EHR using ciphertext‐policy attribute‐based encryption (CP‐ABE) and upload it to the interplanetary file system (IPFS) for storage, while the matching index is encrypted via proxy re‐encryption and kept in a medical consortium blockchain. In addition, we created a credit assessment mechanism and incorporated it into the smart contract. Smart contracts, proxy re‐encryption, a credit evaluation mechanism, and IPFS all work together to give patients with a trustworthy EHR sharing environment and a dynamic access control interface. The thorough comparison and experimental analysis show that the proposed BFHS has more comprehensive security features and is more practicable.

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A Context-Aware Security Model for a Combination of Attribute-Based Access Control and Attribute-Based Encryption in the Healthcare Domain

Cited by 9 publications

References 17 publications

Accessing electronic health records in critical incidents using context-aware attribute-based access control

Accessing electronic health records in critical incidents using context-aware attribute-based access control

Context-Based, Predictive Access Control to Electronic Health Records

A fine‐grained and secure health data sharing scheme based on blockchain

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