Repositori institucional URV
| Autor/es de la URV: | Domingo Ferrer, Josep / Martinez Lluis, Sergio |
| Autor según el artículo: | Domingo-Ferrer, Josep; Muralidhar, Krishnamurty; Martinez, Sergio |
| Direcció de correo del autor: | josep.domingo@urv.cat sergio.martinezl@urv.cat |
| Identificador del autor: | 0000-0001-7213-4962 0000-0002-3941-5348 |
| Año de publicación de la revista: | 2025 |
| Tipo de publicación: | Journal Publications |
| Referencia de l'ítem segons les normes APA: | Domingo-Ferrer, Josep; Muralidhar, Krishnamurty; Martinez, Sergio (2025). Synthetic Data Generation via the Permutation Paradigm With Optional k-Anonymity. Ieee Transactions On Dependable And Secure Computing, 22(3), 3155-3165. DOI: 10.1109/tdsc.2024.3525149 |
| Referencia al articulo segun fuente origial: | Ieee Transactions On Dependable And Secure Computing. 22 (3): 3155-3165 |
| Resumen: | Most methods in the literature on synthetic microdata (individual records) generation are parametric, that is, they require knowing or estimating the joint or the conditional distribution of the original microdata. This may be a significant hurdle unless the original microdata are multivariate normal. We propose a rank-based approach to generating synthetic microdata based on the permutation paradigm. We present three different methods and we analyze the utility and the confidentiality they afford. The third method is actually an extension of the second method that adds k-anonymity protection against reidentification to the confidentiality against attribute disclosure offered by the first two methods. Our algorithms only require the identification of the marginal distributions of attributes and yield synthetic attributes that replicate the relationships between the original attributes exclusively based on ranks. This proposal is especially attractive for non-normal or multi-type microdata. |
| DOI del artículo: | 10.1109/tdsc.2024.3525149 |
| Enlace a la fuente original: | https://ieeexplore.ieee.org/document/10820070 |
| Versión del articulo depositado: | info:eu-repo/semantics/publishedVersion |
| Acceso a la licencia de uso: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departamento: | Enginyeria Informàtica i Matemàtiques |
| URL Documento de licencia: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Áreas temáticas: | Ciência da computação Computer science (all) Computer science (miscellaneous) Computer science, hardware & architecture Computer science, information systems Computer science, software engineering Electrical and electronic engineering Engenharias iii Engenharias iv General computer science |
| Palabras clave: | Anonymizatio Anonymization Computational modeling Confidentiality Covariance matrices Data models Data privacy Data protection Differential privacy Disclosure risk assessment Informatio Measurement Noise Peace, justice and strong institutions Permutation paradigm Prediction algorithms Privacy Proposals Protection Synthetic data Utility |
| Entidad: | Universitat Rovira i Virgili |
| Fecha de alta del registro: | 2025-05-24 |
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| DocumentPrincipal | DocumentPrincipal | application/pdf |
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