Practical and Ready-to-Use Methodology to Assess the re-identification Risk in Anonymized Datasets
This provides a standardized solution for industries needing to comply with privacy policies by assessing anonymization risks, though it is incremental in applying existing cybersecurity methods to this domain.
The paper tackles the lack of a precise methodology for assessing re-identification risk in anonymized datasets by proposing a practical and ready-to-use approach, which incorporates cybersecurity risk analysis methods and attribute qualification to evaluate both attack ability and impact.
To prove that a dataset is sufficiently anonymized, many privacy policies suggest that a re-identification risk assessment be performed, but do not provide a precise methodology for doing so, leaving the industry alone with the problem. This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. EBIOS) that have been used in the cybersecurity field for years, which consider not only the ability to perform an attack, but also the impact such an attack can have on an individual; (2) it is the first to qualify attributes and values of attributes with e.g. degree of exposure, as known real-world attacks mainly target certain types of attributes and not others.