Injecting data into ODRL privacy policies dynamically with RDF mappings
Juan Cano-Benito, Andrea Cimmino, Raúl García‐Castro · 2023
The privacy of the data provided by available sources is one of the major concerns of our era. In order to address this challenge, the W3C has promoted recommendations to allow expressing privacy policies. One of these recommendations is the Open Digital Rights Language (ODRL) vocabulary. Although this standard has wide adoption, it is not suitable in domains such as IoT, Ubiquitous and Mobile Computing, or discovery. The reason behind is the fact that ODRL privacy policies are not able to cope with dynamic information that may come from external sources of data and, therefore, these policies can not define privacy restrictions upon data that is not already written in the policy beforehand. In this demo paper, a solution to this challenge is presented. It is shown how ODRL policies can overcome the aforementioned limitation by being combined with a mapping language for RDF materialisation. The article shows how ODRL policies are able to consider data coming from an external data source when they are solved, in particular, a weather forecast API that provides temperature values. The demonstration defines an ODRL policy that grants access to a resource only when the temperature of the API is above a certain value.