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

Area of Science:

• Health Informatics
• Data Security
• Medical Data Protection

Background:

• The COVID-19 pandemic necessitates robust systems for collecting and analyzing patient data.
• Decision Support Systems (DSS) are vital for accessing COVID-19 patient records for research and prediction.
• Protecting sensitive patient information from unauthorized access is a significant challenge.

Purpose of the Study:

• To propose and evaluate a new technique for enhancing the security of COVID-19 patient data.
• To address the risks associated with unauthorized access to sensitive health information within DSS.

Main Methods:

• Implementation of a two-fold data protection model.
• Utilizing Blowfish encryption for identity attributes.
• Employing pseudonymization techniques to mask identity and quasi-attributes.

Main Results:

• The proposed model effectively encrypts and masks critical patient data attributes.
• All data links, including encrypted, masked, sensitive, and non-sensitive attributes, are secured.
• Enhanced data security against unauthorized access is achieved.

Conclusions:

• The combined approach of Blowfish encryption and pseudonymization offers a robust solution for protecting COVID-19 patient data.
• This method ensures data integrity and confidentiality while enabling necessary data access for research.
• The proposed technique is vital for maintaining patient privacy in the context of pandemic data management.