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

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.

COVID-19 in the Era of Artificial Intelligence: Existing Technologies and A Strategic Model for Mitigating Future Pandemics

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“…For this reason, some hospitals have developed codes of conduct that help doctors and nurses protect patients' privacy when using online platforms (Molnár-Gábor & Korbel, 2020). Hospitals also resort to these codes to update their online corporate communication initiatives and their branding strategies (Fazal et al, 2022). Finally, hospitals also implement training sessions for patients to help them understand how to use online platforms when they interact with doctors (Belani et al, 2021).…”

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