An Approach to Compliance and Security in Healthcare Data Through Privacy and Anonymization Techniques

Abstract

The rapid digitalization of healthcare systems has led to the large-scale generation and exchange of sensitive patient data across clinical, research, and administrative domains. Ensuring the privacy, security, and regulatory compliance of such data remains a major challenge, particularly as healthcare organizations seek to leverage analytics and artificial intelligence for improved decision-making. This paper presents an integrated approach that combines privacy-preserving anonymization techniques with compliance validation mechanisms to strengthen data protection in healthcare environments. The proposed framework employs k-anonymity, differential privacy, and data masking to mitigate re-identification risks while maintaining data utility for legitimate analytical use. A rule-based compliance validation engine is also introduced to ensure adherence to major regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regula- tion (GDPR). Experimental evaluations using synthetic electronic health record (EHR) data demonstrate that the combined application of anonymization techniques achieves significant reductions in re-identification risk with acceptable levels of information loss. The results highlight the potential of the proposed system as a scalable and regulation- aware privacy model for secure healthcare data management.