Conference paper Open Access

Building a Data Processing Activities Catalog: Representing Heterogeneous Compliance-related Information for GDPR using DCAT-AP and DPV

Ryan, Paul; Pandit, Harshvardhan J.; Brennan, Rob

This paper describes a new semantic metadata-based approach to describing and integrating diverse data processing activity descriptions gathered from heterogeneous organisational sources such as departments, divisions, and external processors. This information must be collated to assess and document GDPR legal compliance, such as creating a Register of Processing Activities (ROPA). Most GDPR knowledge graph research to date has focused on developing detailed compliance graphs. However, many organisations already have diverse data collection tools for documenting data processing activities, and this heterogeneity is likely to grow in the future. We provide a new approach extending the well-known DCAT-AP standard utilising the data privacy vocabulary (DPV) to express the concepts necessary to complete a ROPA. This approach enables data catalog implementations to merge and federate the metadata for a ROPA without requiring full alignment or merging all the underlying data sources. To show our approach's feasibility, we demonstrate a deployment use case and develop a prototype system based on diverse data processing records and a standard set of SPARQL queries for a Data Protection Officer preparing a ROPA to monitor compliance. Our catalog's key benefits are that it is a lightweight, metadata-level integration point with a low cost of compliance information integration, capable of representing processing activities from heterogeneous sources.

The ADAPT Centre supports this work for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106_P2) and co-funded under the European Regional Development Fund. Uniphar PLC supports Paul Ryan. Harshvardhan J. Pandit is funded under the Irish Research Council Government of Ireland Postdoctoral Fellowship Grant#GOIPD/2020/790 and the European Union's Horizon 2020 research and innovation programme under NGI TRUST Grant#825618 for Project#3.40 Privacy-as-Expected: Consent Gateway. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission
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