Source Information Disclosure in Ontology-Based Data Integration

Authors: Michael Benedikt, Bernardo Cuenca Grau, Egor Kostylev

AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we formalize 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 lower and upper bounds on disclosure analysis, in the process introducing a number of techniques for analyzing logical privacy issues in ontology-based data integration. Our goal 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 and dataset should satisfy before it is made available to users for querying, as well as on the complexity of checking whether such requirements are fulfilled.
Researcher Affiliation Academia Michael Benedikt, Bernardo Cuenca Grau, and Egor V. Kostylev Department of Computer Science University of Oxford
Pseudocode No The paper describes algorithmic procedures in natural language, such as 'an alternating procedure for checking Comply(O, M, D, p)', but does not include any clearly labeled pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link indicating that source code for the described methodology is publicly available.
Open Datasets No The paper is theoretical and does not conduct empirical experiments using specific datasets. Therefore, it does not mention public datasets for training.
Dataset Splits No The paper is theoretical and does not involve experimental validation with data splits.
Hardware Specification No The paper is theoretical and does not describe any specific hardware used for experiments.
Software Dependencies No The paper focuses on theoretical formalisms and complexity analysis and does not specify any software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings.