A Data Utility-Driven Benchmark for De-identification Methods

• Published in: Trust, Privacy and Security in Digital Business Vol. 11711; pp. 63 - 77
• Main Authors: Tomashchuk, Oleksandr, Van Landuyt, Dimitri, Pletea, Daniel, Wuyts, Kim, Joosen, Wouter
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Anonymisation; Data utility; De-identification; GDPR; Privacy; Pseudonymisation
• ISBN: 3030278123; 9783030278120
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-27813-7_5

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 individual 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, context 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.