Titlebook: Anonymization of Electronic Medical Records to Support Clinical Analysis; Aris Gkoulalas-Divanis,Grigorios Loukides Book 2013 The Author(s

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Introduction,rate the need for sharing such data, in a privacy-preserving way, to support a growing number of medical applications. Subsequently, we consider ., a threat that has led to violations of patients’ privacy. We discuss the challenges that forestalling patient re-identification entails, as well as how

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Overview of Patient Data Anonymization,ion principles that can prevent identity and sensitive information disclosure in demographic data publishing, as well as algorithms for enforcing these principles, in Section.1. Subsequently, in Section 2.2, we turn our attention to diagnosis code anonymization, which, somewhat surprisingly, has not

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Preventing Re-identification While Supporting GWAS,Wide Association Studies (GWAS). After motivating the problem in Sect. 4.1, we provide an overview of an approach that deals with it [5, 9] in Sects. 4.2 and 4.3. This approach extracts potentially linkable clinical features and modifies them in a way that they can no longer be used to link a genomi

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Conclusions and Open Research Challenges, data sharing policies and regulations, which serve as a first line of defence but are unable to provide computational privacy guarantees, and then reviewed several anonymization approaches that can be used to prevent this threat. Specifically, we have surveyed anonymization principles and algorithm