Privacy-preserving healthcare informatics: A review

Last week’s article looked at inference attacks in the cloud, but they happen in other ways as well. In this 2021 paper, Chong reviews the available literature on inference and other privacy attacks that occur on published healthcare information originally sourced from electronic health records (EHRs) and other health informatics systems. Ethical sharing of this kind of data with researchers conducting statistical analysis, improving clinical decision making, etc. is important, but it must preserve the privacy of the underlying individuals. After briefly discussing data publishing with a strong privacy focus, Chong looks at how healthcare data is typically stored and what threats exist against such data. The author then examines two well-established privacy models, including their strengths and limitations, that can be used to limit those threats: data anonymization and differential privacy. Despite these models, Chong closes by noting that “preserving privacy in healthcare data still poses several unsolved privacy and utility challenges” and expounds on areas for future research into those challenges.

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