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在大规模数据共享时代实现决策支持系统的数据隐私保护。

Achieving data privacy for decision support systems in times of massive data sharing.

作者信息

Fazal Rabeeha, Shah Munam Ali, Khattak Hasan Ali, Rauf Hafiz Tayyab, Al-Turjman Fadi

机构信息

Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.

School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H12, Islamabad, Pakistan.

出版信息

Cluster Comput. 2022;25(5):3037-3049. doi: 10.1007/s10586-021-03514-x. Epub 2022 Jan 10.

DOI:10.1007/s10586-021-03514-x
PMID:35035271
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8743442/
Abstract

The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient's data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients' data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

摘要

世界正遭受新型冠状病毒肺炎（Covid-19）大流行的影响，这正在危及人类生命。收集Covid-19患者的记录对于应对这种情况至关重要。决策支持系统（DSS）被用于收集这些记录。研究人员通过DSS访问患者数据，并对Covid-19疾病的严重程度和影响进行预测；然而，未经授权的用户也可能出于恶意目的访问这些数据。因此，保护Covid-19患者数据是一项具有挑战性的任务。在本文中，我们提出了一种保护Covid-19患者数据的新技术。所提出的模型包括两个部分。首先，使用 Blowfish 加密算法对身份属性进行加密。其次，它使用化名处理来掩盖身份和准属性，然后将所有数据相互链接，例如加密的、掩码处理的、敏感的和非敏感的属性。通过这种方式，数据对于未经授权的访问变得更加安全。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/067ac072f813/10586_2021_3514_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/4098d96833df/10586_2021_3514_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/bf28da6c07fe/10586_2021_3514_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/ef8d2b2685d0/10586_2021_3514_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/58963688a1b8/10586_2021_3514_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/6c430a5dac43/10586_2021_3514_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/3d1dee053615/10586_2021_3514_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/92be40102f0e/10586_2021_3514_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/fc10f4395ef0/10586_2021_3514_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/6893443a6d50/10586_2021_3514_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/bb79b7b5c0fb/10586_2021_3514_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/cb3ad06af8f4/10586_2021_3514_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b0/8743442/067ac072f813/10586_2021_3514_Fig13_HTML.jpg

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