2 research outputs found
A data utility-driven benchmark for de-identification methods
De-identification is the process of removing the associations
between data and identifying elements of individual data subjects. Its
main purpose is to allow use of data while preserving the privacy of in-
dividual data subjects. It is thus an enabler for compliance with legal
regulations such as the EU’s General Data Protection Regulation. While
many de-identification methods exist, the required knowledge regarding
technical implications of different de-identification methods is largely
missing. In this paper, we present a data utility-driven benchmark for
different de-identification methods. The proposed solution systematically
compares de-identification methods while considering their nature, con-
text and de-identified data set goal in order to provide a combination of
methods that satisfies privacy requirements while minimizing losses of
data utility. The benchmark is validated in a prototype implementation
which is applied to a real life data set.status: publishe