Enhancing Controlled Query Evaluation through Epistemic Policies

Enhancing Controlled Query Evaluation through Epistemic Policies

Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 3307-3314. https://doi.org/10.24963/ijcai.2024/366

In this paper, we propose the use of epistemic dependencies to express data protection policies in Controlled Query Evaluation (CQE), which is a form of confidentiality-preserving query answering over ontologies and databases. The resulting policy language goes significantly beyond those proposed in the literature on CQE so far, allowing for very rich and practically interesting forms of data protection rules. We show the expressive abilities of our framework and study the data complexity of CQE for (unions of) conjunctive queries when ontologies are specified in the Description Logic DL-LiteR. Interestingly, while we show that the problem is in general intractable, we prove tractability for the case of acyclic epistemic dependencies by providing a suitable query rewriting algorithm. The latter result paves the way towards the implementation and practical application of this new approach to CQE.
Keywords:
Knowledge Representation and Reasoning: KRR: Description logics and ontologies
Knowledge Representation and Reasoning: KRR: Computational complexity of reasoning