Supercomputing and Secure Cloud Infrastructures in Biology and Medicine

2020 ◽  
Vol 3 (1) ◽  
pp. 391-410
Author(s):  
Cathrine Jespersgaard ◽  
Ali Syed ◽  
Piotr Chmura ◽  
Peter Løngreen
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Personalized Medicine ◽  
Data Availability ◽  
End User ◽  
Sensitive Data ◽  
Data Accessibility ◽  
Healthcare Data ◽  
Access To Data ◽  
Cloud Infrastructures

The increasing amounts of healthcare data stored in health registries, in combination with genomic and other types of data, have the potential to enable better decision making and pave the path for personalized medicine. However, reaping the full benefits of big, sensitive data for the benefit of patients requires greater access to data across organizations and institutions in various regions. This overview first introduces cloud computing and takes stock of the challenges to enhancing data availability in the healthcare system. Four models for ensuring higher data accessibility are then discussed. Finally, several cases are discussed that explore how enhanced access to data would benefit the end user.

An Approach to Data Confidentiality Protection in Cloud Environments

2012 ◽  
Vol 9 (3) ◽  
pp. 67-83 ◽  
Author(s):  
Stephen S. Yau ◽  
Ho G. An ◽  
Arun Balaji Buduru
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Data Storage ◽  
Service Providers ◽  
Data Confidentiality ◽  
Sensitive Data ◽  
Computing Systems ◽  
Cloud Application ◽  
Cloud Infrastructures ◽  
Processing Service

In current cloud computing systems, because users’ data is stored and processed by computing systems managed and operated by various service providers, users are concerned with the risks of unauthorized usage of their sensitive data by various entities, including service providers. The current cloud computing systems protect users’ data confidentiality from all entities, except service providers. In this paper, an approach is presented for improving the protection of users’ data confidentiality in cloud computing systems from all entities, including service providers. The authors’ approach has the following features: (1) separation of cloud application providers, data processing service providers and data storage providers, (2) anonymization of users’ identities, (3) grouping cloud application components and distributing their execution to distinct cloud infrastructures of data processing service providers, and (4) use of data obfuscation and cryptography for protecting the sensitive data from unauthorized access by all entities, including service providers. The proposed approach ensures that users’ sensitive data can be protected from their service providers even if the users do not have full cooperation from their service providers.

scholarly journals Secure Data Replication Management Scheme with Better Data Accessibility

2019 ◽  
Vol 8 (12) ◽  
pp. 4484-4488
Keyword(s):  
Cloud Computing ◽  
Data Replication ◽  
Data Availability ◽  
Computing Technology ◽  
Data Accessibility ◽  
Access Frequency ◽  
Management Scheme ◽  
Replica Allocation ◽  
Cloud Server ◽  
Secure Data

Cloud computing technology has gained substantial research interest, due to its remarkable range of services. The major concerns of cloud computing are availability and security. Several security algorithms are presented in the literature for achieving better security and the data availability is increased by utilizing data replicas. However, creation of replicas for all the data is unnecessary and consumes more storage space. Considering this issue, this article presents a Secure Data Replication Management Scheme (SDRMS), which creates replicas by considering the access frequency of the data and the replicas are loaded onto the cloud server by considering the current load of it. This idea balances the load of the cloud server. All the replicas are organized in a tree like structure and the replicas with maximum hit ratio are placed on the first level of the tree to ensure better data accessibility. The performance of the work is satisfactory in terms of data accessibility, storage exploitation, replica allocation and retrieval time.

Opportunistic Edge Computing Architecture for Smart Healthcare Systems

2021 ◽  
pp. 289-306
Author(s):  
Nivethitha V. ◽  
Aghila G.
Keyword(s):  
Cloud Computing ◽  
Healthcare Systems ◽  
Edge Computing ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Network Resource ◽  
Computing Architecture ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Network Response Time

Some of the largest global industries that is driving smart city environments are anywhere and anytime health monitoring applications. Smart healthcare systems need to be more preventive and responsive as they deal with sensitive data. Even though cloud computing provides solutions to the smart healthcare applications, the major challenge imposed on cloud computing is how could the centralized traditional cloud computing handle voluminous data. The existing models may encounter problems related to network resource utilization, overheads in network response time, and communication latency. As a solution to these problems, edge-oriented computing has emerged as a new computing paradigm through localized computing. Edge computing expands the compute, storage, and networking capabilities to the edge of the network which will respond to the above-mentioned issues. Based on cloud computing and edge computing, in this chapter an opportunistic edge computing architecture is introduced for smart provisioning of healthcare data.

scholarly journals Hybrid AES-ECC Model for the Security of Data over Cloud Storage

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2673
Author(s):  
Saba Rehman ◽  
Nida Talat Bajwa ◽  
Munam Ali Shah ◽  
Ahmad O. Aseeri ◽  
Adeel Anjum
Keyword(s):  
Cloud Computing ◽  
Cost Effective ◽  
Third Party ◽  
End User ◽  
Computing Environment ◽  
Sensitive Data ◽  
Cloud Computing Environment ◽  
Private Data ◽  
Remote Storage ◽  
Internet Connections

A cloud computing environment provides a cost-effective way for the end user to store and access private data over remote storage using some Internet connection. The user has access to the data anywhere and at any time. However, the data over the cloud do not remain secure all the time. Since the data are accessible to the end user only by using the interference of a third party, it is prone to breach of authentication and integrity of the data. Moreover, cloud computing allows simultaneous users to access and retrieve their data online over different Internet connections, which leads to the exposure, leakage, and loss of a user’s sensitive data in different locations. Many algorithms and protocols have been developed to maintain the security and integrity of the data using cryptographic algorithms such as the Elliptic Curve Cryptography (ECC). This paper proposes a secure and optimized scheme for sharing data while maintaining data security and integrity over the cloud. The proposed system mainly functions by combining the ECC and the Advanced Encryption Standard (AES) method to ensure authentication and data integrity. The experimental results show that the proposed approach is efficient and yields better results when compared with existing approaches.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

scholarly journals Healthcare Data for Achieving a More Personalized Treatment of Chronic Kidney Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 488
Author(s):  
Francisco Herrera-Gómez ◽  
F. Javier Álvarez
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Prognostic Markers ◽  
Observational Evidence ◽  
Personalized Treatment ◽  
Special Issue ◽  
Healthcare Data ◽  
Clinical Endpoints ◽  
Risk Patients

The current concept of healthcare incites a more personalized treatment of diseases. To this aim, biomarkers are needed to improve decision-making facing chronic kidney disease (CKD) patients. Prognostic markers provided by real-world (observational) evidence are proposed in this Special Issue entitled “Biomarkers in Chronic Kidney Disease”, with the intention to identify high-risk patients. These markers do not target measurable parameters in patients but clinical endpoints that may be in turn transformed to benefits under the effect of future interventions.

scholarly journals Results of the Behavioral Health Readiness Evaluation and Decision-Making Instrument Study

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 142-152
Author(s):  
Justin M Curley ◽  
Katie L Nugent ◽  
Kristina M Clarke-Walper ◽  
Elizabeth A Penix ◽  
James B Macdonald ◽  
...  
Keyword(s):  
Decision Making ◽  
Behavioral Health ◽  
User Satisfaction ◽  
Safety Issue ◽  
Walter Reed Army Institute ◽  
End User ◽  
Chi Square ◽  
Proficiency Level ◽  
Wide Range ◽  
Decision Making Support

ABSTRACT Introduction Recent reports have demonstrated behavioral health (BH) system and individual provider challenges to BH readiness success. These pose a risk to winning on the battlefield and present a significant safety issue for the Army. One of the most promising areas for achieving better BH readiness results lies in improving readiness decision-making support for BH providers. The Walter Reed Army Institute of Research (WRAIR) has taken the lead in addressing this challenge by developing and empirically testing such tools. The results of the Behavioral Health Readiness Evaluation and Decision-Making Instrument (B-REDI) field study are herein described. Methods The B-REDI study received WRAIR Institutional Review Board approval, and BH providers across five U.S. Army Forces Command installations completed surveys from September 2018 to March 2019. The B-REDI tools/training were disseminated to 307 providers through random clinic assignments. Of these, 250 (81%) providers consented to participate and 149 (60%) completed both initial and 3-month follow-up surveys. Survey items included a wide range of satisfaction, utilization, and proficiency-level outcome measures. Analyses included examinations of descriptive statistics, McNemar’s tests pre-/post-B-REDI exposure, Z-tests with subgroup populations, and chi-square tests with demographic comparisons. Results The B-REDI resulted in broad, statistically significant improvements across the measured range of provider proficiency-level outcomes. Net gains in each domain ranged from 16.5% to 22.9% for knowledge/awareness (P = .000), from 11.1% to 15.8% for personal confidence (P = .001-.000), and from 6.2% to 15.1% for decision-making/documentation (P = .035-.002) 3 months following B-REDI initiation, and only one (knowledge) failed to maintain a statistically significant improvement in all of its subcategories. The B-REDI also received high favorability ratings (79%-97% positive) across a wide array of end-user satisfaction measures. Conclusions The B-REDI directly addresses several critical Army BH readiness challenges by providing tangible decision-making support solutions for BH providers. Providers reported high degrees of end-user B-REDI satisfaction and significant improvements in all measured provider proficiency-level domains. By effectively addressing the readiness decision-making challenges Army BH providers encounter, B-REDI provides the Army BH health care system with a successful blueprint to set the conditions necessary for providers to make more accurate and timely readiness determinations. This may ultimately reduce safety and mission failure risks enterprise-wide, and policymakers should consider formalizing and integrating the B-REDI model into current Army BH practice.

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