A SPARQL-based framework to preserve privacy of sensitive data on the semantic web

Over the last few years, the web of data has been evolved. Indeed, it allows sharing of a significant interconnection of a huge amount of data in several domains and it keeps increasing continuously. Due to the confidential nature of some data, sectors such as health, financial, and government, it have limited participation with fewer data to publish. Thus, to develop the web of data and make it more trustworthy, we have to take into consideration the confidentiality, sensitivity, and utility of data. We propose, in this paper, a framework for the confidentiality preservation, and sharing of linked data. Our approach provides the means to specify privacy policies and protect sensitive data in RDF triples. Subsequently, the application of the policy on the graph will allow their replacement by their encryption, which ensures a balance between confidentiality and the utility of data. We have experimented the performance of our proposed solution on benchmarks of different sizes by showing how to preserve the privacy of sensitive data and proving how hard it is to decrypt. The obtained results have shown the effectiveness of our developed framework.