Abstract: With the rapid development of mobile health care, all kinds of medical institutions share personal medical data, but it still has the risk of privacy leak. This paper proposes a personalized entropy privacy protection model to protect the users' privacy with fine granularity based on k-anonymity and l-diversity model. To facilitate medical data sharing security, this model can reduce the leakage probabilities of sensitive information and medical intensive information by distinguishing the strong and weak sensitive attribute values to improve the sense of constraints. The experimental results show that the proposed method can not only enhance the accuracy of the data, but also reduce the execution time. In addition, it also can improve the quality of services and is more effective than existing methods.

Key words: medical information, data publishing, privacy preservation; k-anonymity, l-diversity

摘要： 随着移动医疗的飞速发展，医疗机构在共享个人医疗数据的同时也存在着隐私泄漏的隐患。基于k-匿名和l-多样性模型，提出利用个性化熵l-多样性隐私保护模型来细粒度地保护用户的隐私，通过区分强弱敏感属性值来提高对敏感属性的约束，降低敏感信息及强信息的泄漏概率，从而达到医疗数据共享安全。通过数据分析及实验结果表明，该方法在提高数据精度的同时可以减少执行时间，而且能提高服务质量，比既有的方案更有效。

关键词: 医疗信息, 数据发布, 隐私保护, k-匿名, l-多样性