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Embedding Risk-Based Anonymization into Data Access Control for Providing Individual-Level Health Data in a Secure Way

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Especially in biomedical research, individual-level data must be protected due to the sensitivity of the data that is associated with patients. The broad goal of scientific data re-use is to… Click to show full abstract

Especially in biomedical research, individual-level data must be protected due to the sensitivity of the data that is associated with patients. The broad goal of scientific data re-use is to allow many researchers to derive new hypotheses and insights from the data while preserving privacy. Data usage control (DUC) as an attribute-based access mechanism promises to overcome the limitations of traditional access control models achieving that goal. Park and Sandhu provided the usage control (UCON) model as an instance of DUC, which defines policies that evaluate certain attributes. Here, we present an UCON-based architecture, which is augmented with risk-based anonymization as provided by the R package sdcMicro and an extensible Access Control Markup Language (XACML) environment with a core policy decision point as implemented by authzforce.

Keywords: control; based anonymization; access control; risk based; individual level; access

Journal Title: Studies in health technology and informatics
Year Published: 2022

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