Facilitating Circular Economy (CE)’s monitoring requires access to data from different systems and data spaces. Motivated by this, a number of organizations have established data sharing agreements in line with the European Interoperability Framework to facilitate technical, semantic, organisational, and legal interoperability. Each data space, however, may follow its own domain-specific semantics. While this supports data’s interoperability within the data space, it also poses a challenge in cases such as CE’s monitoring, which requires data from several data spaces to be accessed, combined and analyzed. Supporting findable, accessible, interoperable and reusable (FAIR) data sharing not only within but also between data spaces is key. Ontology alignment can help facilitate semantic interoperability across data spaces and support CE’s monitoring. Following this, we present an upper-ontology-based alignment approach to aid CE’s monitoring in practice. We showcase the implementation of the approach for aligning the FEDeRATED upper-level ontology for data sharing with the RePlanIT (electronics), BattINFO (batteries) ontologies and the Catena-X (cars) data model. As a result, the alignments can be used by parties interested in data sharing between the battery, electronics and car data spaces to generate data sharing agreements, define data access controls and ultimately monitor CE’s implementation. We also share lessons learned from the implementation of the approach and provide a discussion on future directions for semantic-enabled CE monitoring.

Protecting and preserving individuals’ personal data is a legal obligation set out by the European Union’s General Data Protection Regulation (GDPR). However, the process of implementing data governance to support that, in a decentralised ecosystem, is still vague. Motivated by the need for lawful decentralised data processing, this paper outlines several relevant questions from legal, privacy and technology standpoints that need to be considered

The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.

The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies
unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie "disappears", and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine- readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation tool helps increase users’ awareness of cookies and data sharing.