Fog based Secure Framework for Personal Health Records Systems

The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions' repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the burden of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers. Aiming at a massive demand of PHR data within a ubiquitous smart city, we propose a secure and fog assisted framework for PHR systems to address security, access control and privacy concerns. Built under a fog-based architecture, the proposed framework makes use of efficient key exchange protocol coupled with ciphertext attribute based encryption (CP-ABE) to guarantee confidentiality and fine-grained access control within the system respectively. We also make use of digital signature combined with CP-ABE to ensure the system authentication and users privacy. We provide the analysis of the proposed framework in terms of security and performance.Comment: 12 pages (CMC Journal, Tech Science Press

Blockchain for global vaccinations efforts: State of the art, challenges, and future directions

The emergence of the coronavirus disease 2019 (COVID-19) global crisis negatively affected all aspects of human life. One of the most important methods used worldwide to survive this global crisis is the vaccination process to circumvent the proliferation of this pandemic. Many restrictions were alleviated in many countries such as access to public facilities and events. There is a huge amount of data about vaccination campaigns that are collected and maintained worldwide. Although the vaccination data can be analyzed to find out how the alleviation of restrictions can be applied if the data management process requires preserving key aspects like trust, transparency, and availability for easy and reliable access to such data. In this regard, blockchain technology is an excellent choice for meeting the requirements and providing a secure trusted framework for global verification. In this article, the related literature on blockchain technology is surveyed and summarized for all systems that embody solutions. The pros and cons of each solution are presented and provide a comparative summary. Furthermore, a detailed analysis is given to present the current problems and provide a promising mechanism to verify the vaccinated persons anywhere in the world, in a secure manner while retaining individual privacy

An ontology-based compliance audit framework for medical data sharing across Europe

Complying with privacy in multi-jurisdictional health domains is important as well as challenging. The compliance management process will not be efficient unless it manages to show evidences of explicit verification of legal requirements. In order to achieve this goal, privacy compliance should be addressed through “a privacy by design” approach. This paper presents an approach to privacy protection verification by means of a novel audit framework. It aims to allow privacy auditors to look at past events of data processing effectuated by healthcare organisation and verify compliance to legal privacy requirements. The adapted approach used semantic modelling and a semantic reasoning layer that could be placed on top of hospital databases. These models allow the integration of fine-grained context information about the sharing of patient data and provide an explicit capturing of applicable privacy obligation. This is particularly helpful for insuring a seamless data access logging and an effective compliance checking during audit trials

Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions

Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns about data privacy. Solutions based on client-side encryption have been explored to mitigate privacy concerns. Nonetheless, such solutions hinder data processing, specifically clustering, which is pivotal in dealing with different forms of big data. For instance, clustering is critical to limit the search space and perform real-time search operations on big datasets. To overcome the hindrance in clustering encrypted big data, we propose privacy-preserving clustering schemes for three forms of unstructured encrypted big datasets, namely static, semi-dynamic, and dynamic datasets. To preserve data privacy, the proposed clustering schemes function based on statistical characteristics of the data and determine (A) the suitable number of clusters and (B) appropriate content for each cluster. Experimental results obtained from evaluating the clustering schemes on three different datasets demonstrate between 30% to 60% improvement on the clusters' coherency compared to other clustering schemes for encrypted data. Employing the clustering schemes in a privacy-preserving enterprise search system decreases its search time by up to 78%, while increases the search accuracy by up to 35%.Comment: arXiv admin note: text overlap with arXiv:1908.0496

A Step Toward Improving Healthcare Information Integration & Decision Support: Ontology, Sustainability and Resilience

The healthcare industry is a complex system with numerous stakeholders, including patients, providers, insurers, and government agencies. To improve healthcare quality and population well-being, there is a growing need to leverage data and IT (Information Technology) to support better decision-making. Healthcare information systems (HIS) are developed to store, process, and disseminate healthcare data. One of the main challenges with HIS is effectively managing the large amounts of data to support decision-making. This requires integrating data from disparate sources, such as electronic health records, clinical trials, and research databases. Ontology is one approach to address this challenge. However, understanding ontology in the healthcare domain is complex and difficult. Another challenge is to use HIS on scheduling and resource allocation in a sustainable and resilient way that meets multiple conflicting objectives. This is especially important in times of crisis when demand for resources may be high, and supply may be limited. This research thesis aims to explore ontology theory and develop a methodology for constructing HIS that can effectively support better decision-making in terms of scheduling and resource allocation while considering system resiliency and social sustainability. The objectives of the thesis are: (1) studying the theory of ontology in healthcare data and developing a deep model for constructing HIS; (2) advancing our understanding of healthcare system resiliency and social sustainability; (3) developing a methodology for scheduling with multi-objectives; and (4) developing a methodology for resource allocation with multi-objectives. The following conclusions can be drawn from the research results: (1) A data model for rich semantics and easy data integration can be created with a clearer definition of the scope and applicability of ontology; (2) A healthcare system's resilience and sustainability can be significantly increased by the suggested design principles; (3) Through careful consideration of both efficiency and patients' experiences and a novel optimization algorithm, a scheduling problem can be made more patient-accessible; (4) A systematic approach to evaluating efficiency, sustainability, and resilience enables the simultaneous optimization of all three criteria at the system design stage, leading to more efficient distributions of resources and locations for healthcare facilities. The contributions of the thesis can be summarized as follows. Scientifically, this thesis work has expanded our knowledge of ontology and data modelling, as well as our comprehension of the healthcare system's resilience and sustainability. Technologically or methodologically, the work has advanced the state of knowledge for system modelling and decision-making. Overall, this thesis examines the characteristics of healthcare systems from a system viewpoint. Three ideas in this thesis—the ontology-based data modelling approach, multi-objective optimization models, and the algorithms for solving the models—can be adapted and used to affect different aspects of disparate systems