Quotation Pandit, Harshvardan J., Fernandez Garcia, Javier David, Debruyne, Christophe, Polleres, Axel. 2019. Towards Cataloguing Potential Derivations of Personal Data. In The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science, Hrsg. Hitzler P. et al. 147-151. Cham: Springer.


RIS


BibTeX

Abstract

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.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Contribution to conference proceedings
Language English
Title Towards Cataloguing Potential Derivations of Personal Data
Title of whole publication The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science
Editor Hitzler P. et al.
Page from 147
Page to 151
Location Cham
Publisher Springer
Year 2019
ISBN 978-3-030-32326-4
URL https://link.springer.com/chapter/10.1007%2F978-3-030-32327-1_29
Open Access N

Associations

People
Fernandez Garcia, Javier David (Details)
Polleres, Axel (Details)
External
Debruyne, Christophe (ADAPT Centre, Trinity College Dublin, Ireland)
Pandit, Harshvardan J. (ADAPT Centre, Trinity College Dublin, Ireland)
Organization
Applied Information Technology (Details)
Information Business IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1100 Mathematics, information technology (Details)
1109 Information and data processing (Details)
1122 Artificial intelligence (Details)
5367 Management information systems (Details)
5937 Information systems (Details)
Google Scholar: Search