Controlled Query Evaluation in Description Logics Through Instance Indistinguishability
Authors: Gianluca Cima, Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study privacy-preserving query answering in Description Logics (DLs). Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability. We derive data complexity results for query answering over DL-Lite R ontologies, through a comparison with an alternative, existing confidentiality-preserving approach to CQE. Finally, we identify a semantically well-founded notion of approximated query answering for CQE, and prove that, for DL-Lite R ontologies, this form of CQE is tractable with respect to data complexity and is first-order rewritable, i.e., it is always reducible to the evaluation of a first-order query over the data instance. |
| Researcher Affiliation | Academia | Gianluca Cima1 , Domenico Lembo1 , Riccardo Rosati1 and Domenico Fabio Savo2 1Sapienza Universit a di Roma 2Universit a degli Studi di Bergamo {cima, lembo, rosati}@diag.uniroma1.it, domenicofabio.savo@unibg.it |
| Pseudocode | Yes | Algorithm 1: Opt GACensor input: a DL-Lite R TBox T , a policy P, an ABox A; output: an ABox; |
| Open Source Code | No | The paper does not provide a statement or link for open-source code. It mentions future work towards practical implementations: 'We are currently working to achieve this goal.' |
| Open Datasets | No | This paper is theoretical, focusing on logical frameworks, complexity results, and proofs. It does not involve empirical training on datasets. |
| Dataset Splits | No | This paper is theoretical, focusing on logical frameworks, complexity results, and proofs. It does not involve empirical validation on datasets. |
| Hardware Specification | No | This paper is theoretical and does not describe any experimental setup requiring specific hardware specifications. |
| Software Dependencies | No | This paper focuses on theoretical frameworks and algorithms, not an implementation that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |