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Achieving data privacy for decision support systems in times of massive data sharing.
Fazal, Rabeeha; Shah, Munam Ali; Khattak, Hasan Ali; Rauf, Hafiz Tayyab; Al-Turjman, Fadi.
Afiliación
  • Fazal R; Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
  • Shah MA; Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
  • Khattak HA; School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H12, Islamabad, Pakistan.
  • Rauf HT; Department of Computer Science, Faculty of Engineering and Informatics, University of BRADFORD, Bradford, UK.
  • Al-Turjman F; Artificial Intelligence Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Istanbul, Turkey.
Cluster Comput ; 25(5): 3037-3049, 2022.
Article en En | MEDLINE | ID: mdl-35035271
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Cluster Comput Año: 2022 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Cluster Comput Año: 2022 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Países Bajos