Citation:

Narasimha Chaitanya Samineni , 2023. "PII De-Identification Techniques for Healthcare Data Warehouses", ESP Journal of Engineering & Technology Advancements 3(1): 229-236.

Abstract:

Healthcare data warehouses consolidate electronic health records, claims, lab systems, and operational data to support analytics, quality improvement, and research. However, these warehouses often contain protected health information (PHI) and other personally identifiable information (PII) that can be linked across sources, making privacy risk higher than in single-system datasets. This article presents a practical, risk-based framework for de-identifying healthcare warehouse data while preserving analytic utility. The framework aligns regulatory expectations for de-identification, emphasizes realistic threat models, and maps common privacy techniques to healthcare warehouse patterns such as longitudinal patient linkage, cohort discovery, and machine learning feature stores. We describe a technique taxonomy spanning masking, suppression, pseudonymization and tokenization, generalization-based privacy models (k-anonymity family), distributional protections (t-closeness), and differential privacy for aggregate outputs. We then propose a layered warehouse architecture separating a restricted PHI vault from an analytics-ready de-identification zone, supported by strong access control, key management, and auditing. Finally, we provide utility and quality validation methods, operational governance controls, and an implementation blueprint to help organizations deploy de-identification as an engineered capability rather than a one-time data export step. [1][2][4][5][8]

References:

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Keywords:

Healthcare Data Warehouse, PHI, PII, De-identification, Tokenization, k-anonymity, Differential Privacy, Re-identification Risk, Governance. [1][4][5][8]