Data Anonymisation in the light of the General Data Protection Regulation

Bijl, A.F. (2017) Data Anonymisation in the light of the General Data Protection Regulation. Bachelor's Thesis, Computing Science.

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Abstract

The General Data protection Regulation (GDPR) was approved by the European Parliament and Council and will be applied from May 2018 onwards in all countries of the European Union. The GDPR protects the privacy of European citizens by limiting the storage and movement of their personal data. The University of Groningen started a project to analyse the GDPR from a technical perspective. The following thesis is part of this project and takes as subject anonymisation. Anonymisation is a technique to transform information relating to individuals (personal data) to information from which no individual can be identified (anonymous data). As a result organisations are able to publish data to third parties without the sanctions of the GDPR because the GDPR falls outside the scope of anonymous information. Former studies on re-identification have shown that zero risk of re-identification is impossible for useful data. Therefore a trade-off between data privacy and utility has to be made. With this aspect in mind the following thesis analyses and evaluates several anonymisation techniques. The models that are evaluated are k-anonymity, l-diversity, t-closeness and differential privacy. It is observed that no model can guarantee zero risk of re-identification because every model is vulnerable to particular re-identification attacks. Every model can be used to provide more privacy but drops the data utility. The GDPR contains no specifications to satisfy the privacy of data subjects. Therefore organisation must show effort in the direction of privacy. Still it has to seen when the GDPR is applied what effort will be enough.

Item Type:	Thesis (Bachelor's Thesis)
Degree programme:	Computing Science
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	15 Feb 2018 08:31
Last Modified:	15 Feb 2018 08:31
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/15709

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