Abstract
Privacy policies are legal documents that describe activities over personal data such as its collection, usage, processing, sharing, and storage. Expressing this information as provenance metadata can aid in legal accountability as well as modelling of data usage in real-world use-cases. In this paper, we describe our early work on identification, extraction, and representation of provenance information within privacy policies. We discuss the adoption of entity extraction approaches using concepts and keywords defined by the GDPRtEXT resource along with using annotated privacy policy corpus from the UsablePrivacy project. We use the previously published GDPRov ontology (an extension of PROV-O) to model provenance model extracted from privacy policies.
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Pandit, H. J., O’Sullivan, D., & Lewis, D. (2018). Extracting provenance metadata from privacy policies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11017 LNCS, pp. 262–265). Springer Verlag. https://doi.org/10.1007/978-3-319-98379-0_32
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