Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Logical Foundations of Linked Data Anonymisation
Authors: Bernardo Cuenca Grau, Egor V. Kostylev
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we lay the logical foundations of anonymisation in the context of Linked Data. We establish the computational complexity of the underpinning decision problems both under the open-world semantics inherent to RDF and under the assumption that an attacker has complete, closed-world knowledge over some parts of the original data. Our goal in this paper is to lay the theoretical foundations for PPDP in the context of Linked Data, with a focus on the semantic requirements that an anonymised RDF graph should satisfy before being released to Web, as well as on the computational complexity of checking whether such requirements are ful๏ฌlled. |
| Researcher Affiliation | Academia | Bernardo Cuenca Grau EMAIL Egor V. Kostylev EMAIL Department of Computer Science University of Oxford Oxford, United Kingdom. |
| Pseudocode | No | The paper focuses on theoretical foundations, logical frameworks, and computational complexity analysis. It describes methods and proofs using mathematical notation and logical constructs, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses theoretical concepts, logical foundations, and computational complexity. There is no mention of releasing source code, a repository link, or code in supplementary materials for the described methodology. |
| Open Datasets | No | The paper uses an "example RDF graph G0" for illustrative purposes, as described in Section 3.1. This is a small, synthetic example to demonstrate concepts, not a publicly available dataset for experiments. There is no mention of any real-world public or open datasets being used or provided. |
| Dataset Splits | No | The paper is theoretical and focuses on logical foundations and computational complexity. It does not involve empirical experiments with datasets, and therefore, no information about training, test, or validation splits is provided. |
| Hardware Specification | No | The paper primarily presents theoretical work on the logical foundations and computational complexity of Linked Data anonymisation. It does not describe any experiments or computations that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical aspects, logical frameworks, and computational complexity. It does not mention any specific software, libraries, or tools with version numbers that would be required to replicate experimental results or implementations. |
| Experiment Setup | No | The paper is theoretical, laying out logical foundations and analyzing computational complexity. It does not describe any experimental procedures, training configurations, or hyperparameter settings in the main text. |