Gaining access to high-quality health data is a vital requirement to informed decision making for medical practitioners and pharmaceutical researchers. Driven by mutual benefits and regulations, there is a demand for healthcare institutes to share patient data with various parties for research purposes. However, health data in its raw form often contains sensitive information about individuals, and publishing such data will violate their privacy. In this talk I will introduce the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public hospitals, and present the major challenges that make traditional data anonymization methods not applicable. Then I will describe a new privacy model called LKC-privacy, together with an anonymization algorithm, to meet the privacy and information requirements in this BTS case. Experiments on the real-life data demonstrate that the proposed anonymization algorithm can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large dataset.