Assessing the Potentials of LLMs and GANs as State-of-the-Art Tabular Synthetic Data Generation Methods

Miletic, Marko; Sariyar, Murat

Assessing the Potentials of LLMs and GANs as State-of-the-Art Tabular Synthetic Data Generation Methods

URI

https://arbor.bfh.ch/handle/arbor/44714

Version

Published

Date Issued

2024-09-27

Author(s)

Miletic, Marko

Sariyar, Murat

Editor(s)

Domingo-Ferrer, Josep

Universidad Rovira i Virgili

Önen, Melek

EURECOM

Type

Conference Paper

Language

English

Publisher DOI

10.1007/978-3-031-69651-0_25

Journal

Lecture Notes in Computer Science

Privacy in Statistical Databases

ISSN

0302-9743

Publisher URL

https://link.springer.com/chapter/10.1007/978-3-031-69651-0_25

Related URL

https://crises-deim.urv.cat/psd2024/

Organization

Technik und Informatk

Institut für Optimierung und Datenanalyse IODA

Issue

14915

Conference

Privacy in Statistical Databases: International Conference, PSD 2024: Sep 2024 Read More 2024 Proceeding

Publisher

Springer Nature Switzerland

Submitter

Sariyar, Murat

Citation apa

Miletic, M., & Sariyar, M. (2024). Assessing the Potentials of LLMs and GANs as State-of-the-Art Tabular Synthetic Data Generation Methods. In J. Domingo-Ferrer & M. Önen (Eds.), Lecture Notes in Computer Science (Issue 14915). Springer Nature Switzerland. https://arbor.bfh.ch/handle/arbor/44714