84,767 research outputs found
Health data in cloud environments
The process of provisioning healthcare involves massive healthcare data which exists in different forms on disparate data sources and in different formats. Consequently, health information systems encounter interoperability problems at many levels. Integrating these disparate systems requires the support at all levels of a very expensive infrastructures. Cloud computing dramatically reduces the expense and complexity of managing IT systems. Business customers do not need to invest in their own costly IT infrastructure, but can delegate and deploy their services effectively to Cloud vendors and service providers. It is inevitable that electronic health records (EHRs) and healthcare-related services will be deployed on cloud platforms to reduce the cost and complexity of handling and integrating medical records while improving efficiency and accuracy. The paper presents a review of EHR including definitions, EHR file formats, structures leading to the discussion of interoperability and security issues. The paper also presents challenges that have to be addressed for realizing Cloudbased healthcare systems: data protection and big health data management. Finally, the paper presents an active data model for housing and protecting EHRs in a Cloud environment
A Ciphertext Policy Attributes-based Encryption Scheme with Policy Revocation
There are a lot of data exchanges among the parties by
using cloud computing. So data protection is very
important in cloud security environment. Especially, data
protection is needed for all organization by security
services against unauthorized accesses. There are many
security mechanisms for data protection. Attributes-based
Encryption (ABE) is a one-to-many encryption to encrypt
and decrypt data based on user attributes in which the
secret key of a user and the ciphertext are dependent
upon attributes. Ciphertext policy attributes-based
encryption (CP-ABE), an improvement of ABE schemes
performs an access control of security mechanisms for
cloud storage. In this paper, sensitive parts of personal
health records (PHRs) are encrypted by ABE with the
help of CP-ABE. Moreover, an attributes-based policy
revocation case is considered as well as user revocation
and it needs to generate a new secret key. In proposed
policy revocation case, PHRs owner changes attributes
policy to update available user lists. A trusted authority
(TA) is used to issue secret keys as a third party. This
paper emphasizes on key management and it also
improves attributes policy management and user
revocation. Proposed scheme provides a full control on
data owner as much as he changes policy. It supports a
flexible policy revocation in CP-ABE and it saves time
consuming by comparing with traditional CP-ABE
A systematic literature review of cloud computing in eHealth
Cloud computing in eHealth is an emerging area for only few years. There
needs to identify the state of the art and pinpoint challenges and possible
directions for researchers and applications developers. Based on this need, we
have conducted a systematic review of cloud computing in eHealth. We searched
ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as
well as relevant open-access journals for relevant articles. A total of 237
studies were first searched, of which 44 papers met the Include Criteria. The
studies identified three types of studied areas about cloud computing in
eHealth, namely (1) cloud-based eHealth framework design (n=13); (2)
applications of cloud computing (n=17); and (3) security or privacy control
mechanisms of healthcare data in the cloud (n=14). Most of the studies in the
review were about designs and concept-proof. Only very few studies have
evaluated their research in the real world, which may indicate that the
application of cloud computing in eHealth is still very immature. However, our
presented review could pinpoint that a hybrid cloud platform with mixed access
control and security protection mechanisms will be a main research area for
developing citizen centred home-based healthcare applications
Towards A Well-Secured Electronic Health Record in the Health Cloud
The major concerns for most cloud implementers particularly in the health care industry have remained data security
and privacy. A prominent and major threat that constitutes a hurdle for practitioners within the health industry from exploiting and
benefiting from the gains of cloud computing is the fear of theft of patients health data in the cloud. Investigations and surveys
have revealed that most practitioners in the health care industry are concerned about the risk of health data mix-up amongst the
various cloud providers, hacking to comprise the cloud platform and theft of vital patients’ health data.An overview of the
diverse issues relating to health data privacy and overall security in the cloud are presented in this technical report. Based on
identifed secure access requirements, an encryption-based eHR security model for securing and enforcing authorised access to
electronic health data (records), eHR is also presented. It highlights three core functionalities for managing issues relating to
health data privacy and security of eHR in health care cloud
Protection and efficient management of big health data in cloud environment
University of Technology Sydney. Faculty of Engineering and Information Technology.Healthcare data has become a great concern in the academic world and in industry. The deployment of electronic health records (EHRs) and healthcare-related services on cloud platforms will reduce the cost and complexity of handling and integrating medical records while improving efficiency and accuracy. To make effective use of advanced features such as high availability, reliability, and scalability of Cloud services, EHRs have to be stored in the clouds. By exposing EHRs in an outsourced environment, however, a number of serious issues related to data security and privacy, distribution and processing such as the loss of the controllability, different data formats and sizes, the leakage of sensitive information in processing, sensitive-delay requirements has been naturally raised. Many attempts have been made to address the above concerns, but most of the attempts tackled only some aspects of the problem. Encryption mechanisms can resolve the data security and privacy requirements but introduce intensive computing overheads as well as complexity in key distribution. Data is not guaranteed being protected when it is moved from one cloud to another because clouds may not use equivalent protection schemes. Sensitive data is being processed at only private clouds without sufficient resources. Consequently, Cloud computing has not been widely adopted by healthcare providers and users. Protecting and managing health data efficiently in many aspects is still an open question for current research.
In this dissertation, we investigate data security and efficient management of big health data in cloud environments. Regarding data security, we establish an active data protection framework to protect data; we investigate a new approach for data mobility; we propose trusted evaluation for cloud resources in processing sensitive data. For efficient management, we investigate novel schemes and models in both Cloud computing and Fog computing for data distribution and data processing to handle the rapid growth of data, higher security on demand, and delay requirements.
The novelty of this work lies in the novel data mobility management model for data protection, the efficient distribution scheme for a large-scale of EHRs, and the trust-based scheme in security and processing. The contributions of this thesis can be summarized according to data security and efficient data management.
On data security, we propose a data mobility management model to protect data when it is stored and moved in clouds. We suggest a trust-based scheduling scheme for big data processing with MapReduce to fulfil both privacy and performance issues in a cloud environment.
• The data mobility management introduces a new location data structure into an active data framework, a Location Registration Database (LRD), protocols for establishing a clone supervisor and a Mobility Service (MS) to handle security and privacy requirements effectively. The model proposes a novel security approach for data mobility and leads to the introduction of a new Data Mobility as a Service (DMaaS) in the Cloud.
• The Trust-based scheduling scheme investigates a novel composite trust metric and a real-time trust evaluation for cloud resources to provide the highest trust execution on sensitive data. The proposed scheme introduces a new approach for big data processing to meet with high security requirements.
On the efficient data management, we propose a novel Hash-Based File Clustering (HBFC) scheme and data replication management model to distribute, store and retrieve EHRs efficiently. We propose a data protection model and a task scheduling scheme which is Region-based for Fog and Cloud to address security and local performance issues.
• The HBFC scheme innovatively utilizes hash functions to cluster files in defined clusters such that data can be stored and retrieved quickly while maintaining the workload balance efficiently. The scheme introduces a new clustering mechanism in managing a large-scale of EHRs to deliver healthcare services effectively in the cloud environment.
• The trust-based scheduling model uses the proposed trust metric for task scheduling with MapReduce. It not only provides maximum trust execution but also increases resource utilization significantly. The model suggests a new trust-oriented scheduling mechanism between tasks and resources with MapReduce.
• We introduce a novel concept “Region” in Fog computing to handle the data security and local performance issues effectively. The proposed model provides a novel Fog-based Region approach to handle security and local performance requirements.
We implement and evaluate our proposed models and schemes intensively based on both real infrastructures and simulators. The outcomes demonstrate the feasibility and the efficiency of our research in this thesis. By proposing innovative concepts, metrics, algorithms, models, and services, the significant contributions of this thesis enable both healthcare providers and users to adopt cloud services widely, and allow significant improvements in providing better healthcare services
DATUM in Action
This collaborative research data management planning project (hereafter the RDMP project) sought to help a collaborative group of researchers working on an EU FP7 staff exchange project (hereafter the EU project) to define and implement good research data management practice by developing an appropriate DMP and supporting systems and evaluating their initial implementation. The aim was to "improve practice on the ground" through more effective and appropriate systems, tools/solutions and guidance in managing research data. The EU project (MATSIQEL - (Models for Ageing and Technological Solutions For Improving and Enhancing the Quality of Life), funded under the Marie Curie International Research Staff Exchange Scheme, is accumulating expertise for the mathematical and computer modelling of ageing processes with the aim of developing models which can be implemented in technological solutions (e.g. monitors, telecare, recreational games) for improving and enhancing quality of life.1 Marie Curie projects do not fund research per se, so the EU project has no resources to fund commercial tools for research data management. Lead by Professor Maia Angelova, School of Computing, Engineering and Information Sciences (SCEIS) at Northumbria University, it comprises six work packages involving researchers at Northumbria and in Australia, Bulgaria, Germany, Mexico and South Africa. The RDMP project focused on one of its work packages (WP4 Technological Solutions and Implementation) with some reference to another work package lead by the same person at Northumbria University (WP5 Quality of Life).
The RDMP project‟s innovation was less about the choice of platform/system, as it began with existing standard office technology, and more about how this can be effectively deployed in a collaborative scenario to provide a fit-for-purpose solution with useful and usable support and guidance. It built on the success of the Datum for Health project by taking it a stage further, moving from a solely health discipline to an interdisciplinary context of health, social care and mathematical/computer modelling, and from a Postgraduate Research Student context to an academic researcher context, with potential to reach beyond the University boundaries. In addition, since the EU project is re-using data from elsewhere as well as creating its own data; a wide range of RDM issues were addressed. The RDMP project assessed the transferability of the DATUM materials and the tailored DATUM DMP
Visions and Challenges in Managing and Preserving Data to Measure Quality of Life
Health-related data analysis plays an important role in self-knowledge,
disease prevention, diagnosis, and quality of life assessment. With the advent
of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices
(wearables, home-medical sensors, etc) facilitates data collection and provide
cloud storage with a central administration. More recently, blockchain and
other distributed ledgers became available as alternative storage options based
on decentralised organisation systems. We bring attention to the human data
bleeding problem and argue that neither centralised nor decentralised system
organisations are a magic bullet for data-driven innovation if individual,
community and societal values are ignored. The motivation for this position
paper is to elaborate on strategies to protect privacy as well as to encourage
data sharing and support open data without requiring a complex access protocol
for researchers. Our main contribution is to outline the design of a
self-regulated Open Health Archive (OHA) system with focus on quality of life
(QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System
Secure and Trustable Electronic Medical Records Sharing using Blockchain
Electronic medical records (EMRs) are critical, highly sensitive private
information in healthcare, and need to be frequently shared among peers.
Blockchain provides a shared, immutable and transparent history of all the
transactions to build applications with trust, accountability and transparency.
This provides a unique opportunity to develop a secure and trustable EMR data
management and sharing system using blockchain. In this paper, we present our
perspectives on blockchain based healthcare data management, in particular, for
EMR data sharing between healthcare providers and for research studies. We
propose a framework on managing and sharing EMR data for cancer patient care.
In collaboration with Stony Brook University Hospital, we implemented our
framework in a prototype that ensures privacy, security, availability, and
fine-grained access control over EMR data. The proposed work can significantly
reduce the turnaround time for EMR sharing, improve decision making for medical
care, and reduce the overall costComment: AMIA 2017 Annual Symposium Proceeding
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