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Service oriented architecture and privacy preserving mashup of healthcare data

机译:面向服务的体系结构和医疗数据的隐私保护混搭

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Mashup of health care data from different medical sources must be privacy preserved since the data recipient and/or the data provider may not always be a trusted party. Raw medical data contains person specific sensitive information like ailment, surgery etc. and hence it is susceptible to certain privacy attacks such as attribute linkage and record linkage. There are different privacy models to thwart the privacy attacks. This paper illustrates how to vertically integrate the data from mental health clinic and National AIDS Control Organization (NACO) and preserve privacy using the LKC privacy model. Keywords : Mashup, Linkage, Anonymize Utility, Diversity, Adversary, Interoperability.
机译:由于数据接收者和/或数据提供者可能并不总是受信任方,因此必须保护来自不同医学来源的医疗数据的混搭。原始医疗数据包含特定于人的敏感信息,例如疾病,手术等,因此容易受到某些隐私攻击的侵害,例如属性链接和记录链接。有不同的隐私模型可以阻止隐私攻击。本文说明了如何垂直整合来自精神卫生诊所和美国国家艾滋病控制组织(NACO)的数据并使用LKC隐私模型保护隐私。关键字:混搭,链接,匿名实用程序,多样性,对手,互操作性。

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