Enhancing Privacy in Healthcare: A Multilevel Approach to (Pseudo)Anonymization

Rapid technological advancement has revolutionized the acquisition, processing, and storage of personal data, with notable data breaches from significant corporations emphasizing the value of data and the need for enhanced privacy protection. This has led to a global focus on individual privacy by enacting privacy-centric laws. The healthcare sector, known for its data sensitivity, presents distinct challenges necessitating stringent privacy protocols. The healthcare sector, known for its data sensitivity, presents distinct challenges necessitating stringent privacy protocols. Thus, there is a critical need for robust data privacy measures, including (pseudo)anonymization, to address this shift. This paper introduces a tailored multilevel (pseudo)anonymization architecture designed for healthcare data, capable of ensuring secure data handling and precise anonymization across various sources, even in a (pseudo)anonymized state. The proposed architecture was developed as a proof of concept and underwent thorough evaluation through a series of experiments. The outcomes are encouraging by showcasing effectiveness in achieving accurate anonymization, secure data linkage, and supporting re-identification when essential for individual security.