Making Sense of Consent with Knowledge Graphs

The General Data Protection Regulation (GDPR) [1], which came into effect in May 2018, triggered a major technological shift towards greater transparency in data sharing [2]. An emphasis has been put on the rights of individuals, especially European citizens, regarding their personal data sharing. Despite the fact that data sharing has been a widely researched topic for years, there is a lack of solutions that enable the transparent implementation of consent in an easily understandable manner for both humans and machines in compliance with GDPR. This thesis presents a knowledge graphbased approach for consent representation and implementation that supports machines and humans in making sense of consent through its entire life-cycle. This is achieved by combining approaches from the computer science and behavioral change fields and by considering the comprehension needs of both humans and machines. This thesis demonstrates the feasibility of the approach through its successful adoption and implementation in two industrial data sharing use cases in the smart cities and insurance domains from the smashHit and CampaNeo projects. The main objectives of this work are to use knowledge graphs to semantically represent the life-cycle of informed consent and to visually represent it to individuals in effort to increase legal awareness of the significance of consent [3]. The visualisations place emphasis on the pre- and post-consent stages, including how to request consent in an informed manner and what happens to one's data after consent is given. Incentives, in the form of gamification, are further used to overcome issues such as blindly given consent and to raise the consent rates.