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 Privacy-Preserving Publishing of Linked Data
Authors: Bernardo Cuenca Grau, Egor Kostylev
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we lay the foundations of privacy-preserving data publishing (PPDP) in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and deο¬ne notions of safe and optimal anonymisations. We establish the complexity of the underpinning decision problems both under open-world semantics inherent to RDF and a closed-world semantics |
| Researcher Affiliation | Academia | Bernardo Cuenca Grau and Egor V. Kostylev Department of Computer Science, University of Oxford, UK |
| Pseudocode | No | The paper describes logical definitions, theorems, and complexity results, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code is made available. |
| Open Datasets | No | The paper is theoretical and focuses on logical foundations and computational complexity; it does not describe empirical studies involving datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no validation splits are described. |
| Hardware Specification | No | This is a theoretical paper discussing computational complexity. It does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper. It does not mention any specific software dependencies with version numbers related to running experiments. |
| Experiment Setup | No | This is a theoretical paper. It does not describe any experimental setup details such as hyperparameters or training configurations. |