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Centralized and Distributed Anonymization for High-Dimensional Healthcare Data NOMAN MOHAMMED and BENJAMIN C. M. FUNG Concordia University PATRICK C. K. HUNG University of Ontario Institute of Technology and CHEUK-KWONG LEE Hong Kong Red Cross Blood Transfusion Service 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 L KC-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. Categories and Subject Descriptors: H.2.7 [Database Management]: Database Administration security, integrity, and protection; H.2.8 [Database Management]: Database Applications Data mining General Terms:
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Oct 1, 2010
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