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Towards the design of a comprehensive data de-identification solution

The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources. The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors. The de-identification process also comes with the limitation that all the data cannot be de-identified for an object as it might affect future research. Also, if the exact data which is needed for the research are kept original then it may re-identify the person. To this effect, this research presents the design of a comprehensive data de-identification solution and evaluates the strengths of the application.