Logical Foundations of Privacy-Preserving Publishing of Linked Data
Authors: Bernardo Cuenca Grau, Egor Kostylev
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 define 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. |