IFHDS: Intelligent Framework for Securing Healthcare BigData.

Clicks: 437
ID: 51758
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #9 of 134 articles by views in Journal of medical systems

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 134 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Big data has become one of the most imperative technologies for collecting, handling and analysing enormous volumes of data in a high-performance environment. Enterprise healthcare organizations needs high compute power for the large volume of sensitive data, as well as large storage for storing both data and results, preferably in the cloud. However, security and privacy of patient data have become a critical issue that restricts many healthcare services from using cloud services to their optimal level. Therefore, this issue has limited healthcare organizations from migrating patient data to a cloud storage, because the cloud operators have chance to access sensitive data without the owner's permission. This paper proposes an intelligent security system called Intelligent Framework for Healthcare Data Security (IFHDS). IFHDS enables to secure and process large-scale data using column-based approach with less impact on the performance of data processing. The intelligent framework intends masking personal data and to encrypt sensitive data only. The proposed IFHDS splits sensitive data into multiple parts according to sensitivity level, where each part is stored separately over distributed cloud storage. Splitting data based on sensitivity level prevents cloud provider to break complete record of data if succeeds to decrypt part of data. The experimental results confirm that the proposed system secure the sensitive patient data with an acceptable computation time compared to recent security approaches.
Reference Key
essa2019ifhdsjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Essa, Youssef M;Hemdan, Ezz El-Din;El-Mahalawy, Ahmed;Attiya, Gamal;El-Sayed, Ayman;
Journal Journal of medical systems
Year 2019
DOI
10.1007/s10916-019-1250-4
URL
Keywords
healthcare big data security cloud computing

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.