The exponential growth of data-intensive financial and health analytics platforms has intensified concerns surrounding cybersecurity, data privacy, and ethical governance. Conventional privacy protection strategies often emphasize system-level controls while overlooking the human-centric aspects of privacy perception, autonomy, and informed consent. This review investigates the evolution and implementation of human-centered privacy protection frameworks, focusing on their integration within cyber governance models across financial and healthcare domains. Emphasis is placed on user-centric design principles, privacy-by-design architecture, differential privacy, and the role of transparency-enhancing technologies in building trust. The paper also evaluates emerging privacy-preserving machine learning techniques, regulatory compliance models (such as HIPAA and GDPR), and adaptive access control mechanisms that align with dynamic user behaviors. Through comparative analysis and case studies, the study highlights how embedding human-centered ethics and usability into cyber governance enhances system resilience, fosters accountability, and mitigates privacy risks in critical infrastructure analytics. The findings underscore the necessity for harmonized frameworks that prioritize user agency while ensuring regulatory and technical robustness.
Taiwo et al. (Fri,) studied this question.
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