PURE: A Privacy Aware Rule-Based Framework over Knowledge Graphs

Open data initiatives and FAIR data principles have encouraged the publication of large volumes of data, encoding knowledge relevant for the advance of science and technology. However, to mine knowledge, it is usually required the processing of data collected from sources regulated by diverse access and privacy policies. We address the problem of enforcing data privacy and access regulations (EDPR) and propose PURE, a framework able to solve this problem during query processing. PURE relies on the local as view approach for defining the rules that represent the access control policies imposed over a federation of RDF knowledge graphs. Moreover, PURE maps the problem of checking if a query meets the privacy regulations to the problem of query rewriting (QRP) using views; it resorts to state-of-the-art QRP solutions for determining if a query violates or not the defined policies. We have evaluated the efficiency of PURE over the Berlin SPARQL Benchmark (BSBM). Observed results suggest that PURE is able to scale up to complex scenarios where a large number of rules represents diverse types of policies.