↓ Skip to main content

Distributed clinical data sharing via dynamic access-control policy transformation

Overview of attention for article published in International Journal of Medical Informatics, February 2016
Altmetric Badge

Mentioned by

twitter
1 X user

Readers on

mendeley
113 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Distributed clinical data sharing via dynamic access-control policy transformation
Published in
International Journal of Medical Informatics, February 2016
DOI 10.1016/j.ijmedinf.2016.02.002
Pubmed ID
Authors
Abstract

Data sharing in electronic health record (EHR) systems is important for improving the quality of healthcare delivery. Data sharing, however, has raised some security and privacy concerns because healthcare data could be potentially accessible by a variety of users, which could lead to privacy exposure of patients. Without addressing this issue, large-scale adoption and sharing of EHR data are impractical. The traditional solution to the problem is via encryption. Although encryption can be applied to access control, it is not applicable for complex EHR systems that require multiple domains (e.g. public and private clouds) with various access requirements. This study was carried out to address the security and privacy issues of EHR data sharing with our novel access-control mechanism, which captures the scenario of the hybrid clouds and need of access-control policy transformation, to provide secure and privacy-preserving data sharing among different healthcare enterprises. We introduce an access-control mechanism with some cryptographic building blocks and present a novel approach for secure EHR data sharing and access-control policy transformation in EHR systems for hybrid clouds. We propose a useful data sharing system for healthcare providers to handle various EHR users who have various access privileges in different cloud environments. A systematic study has been conducted on data sharing in EHR systems to provide a solution to the security and privacy issues. In conclusion, we introduce an access-control method for privacy protection of EHRs and EHR policy transformation that allows an EHR access-control policy to be transformed from a private cloud to a public cloud. This method has never been studied previously in the literature. Furthermore, we provide a protocol to demonstrate policy transformation as an application scenario.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 113 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 20 18%
Student > Master 20 18%
Researcher 12 11%
Lecturer 5 4%
Student > Doctoral Student 5 4%
Other 22 19%
Unknown 29 26%
Readers by discipline
Readers by discipline Count As %
Computer Science 38 34%
Medicine and Dentistry 13 12%
Engineering 10 9%
Nursing and Health Professions 5 4%
Business, Management and Accounting 4 4%
Other 6 5%
Unknown 37 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 March 2016.
All research outputs
#28,235,023
of 33,992,632 outputs
Outputs from International Journal of Medical Informatics
#1,809
of 2,219 outputs
Outputs of similar age
#356,795
of 456,493 outputs
Outputs of similar age from International Journal of Medical Informatics
#21
of 25 outputs
Altmetric has tracked 33,992,632 research outputs across all sources so far. This one is in the 9th percentile – i.e., 9% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,219 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 456,493 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.