Towards Cataloguing Potential Derivations of Personal Data

The General Data Protection Regulation (GDPR) has established transparency and accountability in the context of personal data usage and collection. While its obligations clearly apply to data explicitly obtained from data subjects, the situation is less clear for data derived from existing personal...

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Bibliographic Details
Published inThe Semantic Web: ESWC 2019 Satellite Events Vol. 11762; pp. 147 - 151
Main Authors Pandit, Harshvardhan J., Fernández, Javier D., Debruyne, Christophe, Polleres, Axel
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Derived data
GDPR
Personal data
Semantic web
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Summary:The General Data Protection Regulation (GDPR) has established transparency and accountability in the context of personal data usage and collection. While its obligations clearly apply to data explicitly obtained from data subjects, the situation is less clear for data derived from existing personal data. In this paper, we address this issue with an approach for identifying potential data derivations using a rule-based formalisation of examples documented in the literature using Semantic Web standards. Our approach is useful for identifying risks of potential data derivations from given data and provides a starting point towards an open catalogue to document known derivations for the privacy community, but also for data controllers, in order to raise awareness in which sense their data collections could become problematic.
ISBN:3030323269
9783030323264
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-32327-1_29