Logical Foundations of Information Disclosure in Ontology-Based Data Integration

Publication date: Available online 15 June 2018
Source:Artificial Intelligence
Author(s): Michael Benedikt, Bernardo Cuenca Grau, Egor V. Kostylev

Ontology-based data integration systems allow users to effectively access data sitting in multiple sources by means of queries over a global schema described by an ontology. In practice, data sources often contain sensitive information that the data owners want to keep inaccessible to users. Our aim in this paper is to lay the logical foundations of information disclosure in ontology-based data integration. Our focus is on the semantic requirements that a data integration system should satisfy before it is made available to users for querying, as well as on the computational complexity of checking whether such requirements are fulfilled. In particular, we formalise and study the problem of determining whether a given data integration system discloses a source query to an attacker. We consider disclosure on a particular dataset, and also whether a schema admits a dataset on which disclosure occurs. We provide matching lower and upper complexity bounds on disclosure analysis, in the process introducing a number of techniques for analysing logical privacy issues in ontology-based data integration.

from ScienceDirect Publication: Artificial Intelligence https://ift.tt/2HREiYV

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