Abstract
We study information disclosure in Description Logic ontologies, in the spirit of Controlled Query Evaluation, where query answering is filtered through optimal censors maximizing answers while hiding data protected by a declarative policy. Previous works have considered limited forms of policy, typically constituted by conjunctive queries (CQs), whose answer must never be inferred by a user. Also, existing implementations adopt approximated notions of censors that might result too restrictive in the practice in terms of the amount of non-protected information returned to the users. In this paper we enrich the framework, by extending CQs in the policy with comparison predicates and introducing preferences between ontology predicates, which can be exploited to decide the portion of a secret that can be disclosed to a user, thus in principle augmenting the throughput of query answers. We show that answering CQs in our framework is first-order rewritable for \(\textit{DL-Lite}_{A} \) ontologies and safe policies, and thus in AC \(^0\) in data complexity. We also present some experiments on a popular benchmark, showing effectiveness and feasibility of our approach in a real-world scenario.
This work was partly supported by the ANR AI Chair INTENDED (ANR-19-CHIA-0014), by the EU within the H2020 Programme under the grant agreement 834228 (ERC Advanced Grant WhiteMec) and the grant agreement 825333 (MOSAICrOWN), by Regione Lombardia within the Call Hub Ricerca e Innovazione under the grant agreement 1175328 (WATCHMAN), and by the Italian MUR (Ministero dell’Università e della Ricerca) through the PRIN project HOPE (prot. 2017MMJJRE), and by Sapienza (project CQEinOBDM).
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Notes
- 1.
This example is inspired by the benchmark we use in the experiments.
- 2.
For the sake of presentation, we consider here CQE over ontologies. Our extensions and results apply straightforwardly to a privacy-protected OBDA framework [6].
- 3.
- 4.
A similar result is provided in [3, Theorem 38] in the context of CQA.
- 5.
Technically speaking, \( \mathsf {PerfectRef}\) rewrites CQs. We here adopt a variant that rewrites the positive part of each \(\textit{BCQ}_ ineq \) in the premise of a policy assertion, which provides a correct reformulation under the safe policy assumption.
- 6.
- 7.
In [6], we have extracted the conjunctive component of each such query, which in NPD contains also aggregate operators.
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Cima, G., Lembo, D., Marconi, L., Rosati, R., Savo, D.F. (2021). Controlled Query Evaluation over Prioritized Ontologies with Expressive Data Protection Policies. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_22
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