Centralized and Distributed Anonymization for High-Dimensional Healthcare Data

TLDR

Sharing healthcare data is essential for system management, yet improper sharing threatens patient privacy. The study investigates privacy concerns in sharing patient information between the Hong Kong Red Cross Blood Transfusion Service and public hospitals. The authors generalize the privacy requirements to centralized and distributed anonymization, identify challenges, and introduce the LKC‑privacy model with two algorithms for both scenarios. Experiments on real‑life data show that the algorithms preserve essential information for analysis while scaling to large datasets.

Abstract

Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients’ privacy. In this article, we study the privacy concerns of sharing patient information between the Hong Kong Red Cross Blood Transfusion Service (BTS) and the public hospitals. We generalize their information and privacy requirements to the problems of centralized anonymization and distributed anonymization , and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy to overcome the challenges and present two anonymization algorithms to achieve LKC-privacy in both the centralized and the distributed scenarios. Experiments on real-life data demonstrate that our anonymization algorithms can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.