Preventing Unintended Disclosure of Personally Identifiable Data Following Anonymisation
Chris Smith · Studies in health technology and informatics · 2017
Errors and anomalies during the capture and processing of health data have the potential to place personally identifiable values into attributes of a dataset that are expected to contain non-identifiable values. Anonymisation focuses on those attributes that have been judged to enable identification of individuals. Attributes that are judged to contain non-identifiable values are not considered, but may be included in datasets that are shared by organisations. Consequently, organisations are at risk of sharing datasets that unintendedly disclose personally identifiable values through these attributes. This would have ethical and legal implications for organisations and privacy implications for individuals whose personally identifiable values are disclosed. In this paper, we formulate the problem of unintended disclosure following anonymisation, describe the necessary steps to address this problem, and discuss some key challenges to applying these steps in practice.