It is generally conceded that, due to security and privacy concerns, enterprises and users are reluctant to embrace the cloud computing paradigm and hence benefit from the cost reductions and the increased flexibility or business agility that this paradigm brings about. These concerns stem mainly from the significantly-expanded attack surfaces that result from the heterogeneous nature of cloud services and the dynamicity inherent in cloud environments. In order to alleviate these concerns, effective and flexible access control approaches are required to consider the contextual parameters that characterise data access requests in the cloud. In this respect, this work presents PaaSword: a novel holistic access control framework-essentially a PaaS offering-that extends the popular XACML standard with semantic reasoning capabilities that support the federation of effective context-aware access control policies and their infusion into cloud applications with minimal manual intervention and effort. To determine the performance of our solution, a comparative evaluation test is presented and discussed, against a well-known reference implementation of the XACML standard, namely the open source WSO2 Balana engine.

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.

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.