Big Health Data Resource Integration Method Based on Hybrid Cloud and Fog Computing

2020 ◽  
Author(s):  
Xiaodong Zhang ◽  
Xiaojun Xia ◽  
Miao Leng
Keyword(s):  
Integration Method ◽  
Fog Computing ◽  
Health Data ◽  
Resource Integration ◽  
Hybrid Cloud ◽  
Data Resource

scholarly journals A Comprehensive Survey on Security and Privacy for Electronic Health Data

2021 ◽  
Vol 18 (18) ◽  
pp. 9668
Author(s):  
Se-Ra Oh ◽  
Young-Duk Seo ◽  
Euijong Lee ◽  
Young-Gab Kim
Keyword(s):  
Health Systems ◽  
State Of The Art ◽  
Fog Computing ◽  
Health Data ◽  
Security And Privacy ◽  
Health Security ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Edge And Fog Computing ◽  
Privacy Issues

Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.

scholarly journals Data Resource: Vascular Risk in Adult New Zealanders (VARIANZ) datasets

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Suneela Mehta ◽  
Rod Jackson ◽  
Daniel J Exeter ◽  
Billy P Wu ◽  
Sue Wells ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
New Zealand ◽  
Smoking Status ◽  
Health Data ◽  
Service Utilisation ◽  
Vascular Risk ◽  
Cvd Risk ◽  
Data Resource ◽  
Individual Level ◽  
New Zealanders

Introduction The Vascular Risk in Adult New Zealanders (VARIANZ) datasets contain a range of routinely-collected New Zealand health data relevant to cardiovascular disease (CVD) and related conditions. The datasets enable exploration of cardiovascular-related treatment, service utilisation, outcomes and prognosis. Processes Each dataset is constructed by anonymised individual-level linkage of eight national administrative health databases to identify all New Zealand adults aged 20 years and older who have recorded contact with publicly-funded New Zealand health services during a given year from 2006 onwards, when data quality is considered sufficient. Data contents Individual-level data for each VARIANZ dataset includes variables covering demography, dispensing of cardiovascular disease (CVD) preventive medications, prior hospitalisations for atherosclerotic CVD, heart failure, atrial fibrillation, diabetes and five-year risk of a fatal or non-fatal CVD event for those without prior CVD. If required, VARIANZ datasets can be individually linked to follow-up national routinely collected health data in subsequent years, including all-cause mortality events, fatal and non-fatal CVD events and dispensing of cardiovascular medications, to create VARIANZ longitudinal cohorts. Bespoke linkage can also be undertaken to include other national and regional administrative health data such as non-CVD related hospitalisations, or to identify a subset of the VARIANZ datasets based on specific health contacts such as CVD-related hospitalisations only. The New Zealand routinely-collected health databases used to construct the VARIANZ datasets do not capture primary care diagnostic classifications or certain CVD risk factor data such as smoking status, blood pressure or lipid profiles. Conclusions The Vascular Risk in Adult New Zealanders (VARIANZ) datasets capture the majority of the New Zealand population in a given year and are available for any year from 2006 onwards. VARIANZ data can be used to explore a range of research questions regarding management, outcomes and prognosis for CVD.

scholarly journals Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model

10.2196/18920 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18920
Author(s):  
Adrian Paul Brown ◽  
Sean M Randall
Keyword(s):  
Cloud Computing ◽  
Record Linkage ◽  
Social Issues ◽  
Cloud Model ◽  
Synthetic Data ◽  
Health Data ◽  
Data Sets ◽  
Hybrid Cloud ◽  
Cloud Infrastructure ◽  
Amazon Web Services

Background The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. Objective This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. Methods A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. Results The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). Conclusions The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

scholarly journals Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model (Preprint)

2020 ◽  
Author(s):  
Adrian Paul Brown ◽  
Sean M Randall
Keyword(s):  
Cloud Computing ◽  
Record Linkage ◽  
Social Issues ◽  
Cloud Model ◽  
Synthetic Data ◽  
Health Data ◽  
Data Sets ◽  
Hybrid Cloud ◽  
Cloud Infrastructure ◽  
Amazon Web Services

BACKGROUND The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. OBJECTIVE This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. METHODS A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. RESULTS The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). CONCLUSIONS The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

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