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Authors: Mohammad Emadi 1 ; Vahideh Moghtadaiee 1 and Mina Alishahi 2

Affiliations: 1 Cyberspace Research Institute, Shahid Beheshti University, Tehran, Iran ; 2 Department of Computer Science, Open Universiteit, The Netherlands

Keyword(s): Data Privacy, GAN, DPGAN, Synthetic Data, Classifier.

Abstract: Generative Adversarial Networks (GANs) and Differentially Private GANs (DPGANs) have emerged as powerful tools for generating synthetic datasets while preserving privacy. In this work, we investigate the impact of using GAN- and DPGAN-generated datasets on the performance of machine learning classifiers. We generate synthetic datasets using both models and train a variety of classifiers to evaluate their accuracy and robustness on multiple benchmark datasets. We compare classifier performance on real versus synthetic datasets in four different evaluation scenarios. Our results provide insights into the feasibility of using GANs and DPGANs for privacy-preserving data generation and their implications for machine learning tasks.

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Paper citation in several formats:
Emadi, M., Moghtadaiee, V., Alishahi and M. (2025). From Real to Synthetic: GAN and DPGAN for Privacy Preserving Classifications. In Proceedings of the 22nd International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-760-3; ISSN 2184-7711, SciTePress, pages 711-716. DOI: 10.5220/0013566000003979

@conference{secrypt25,
author={Mohammad Emadi and Vahideh Moghtadaiee and Mina Alishahi},
title={From Real to Synthetic: GAN and DPGAN for Privacy Preserving Classifications},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - SECRYPT},
year={2025},
pages={711-716},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013566000003979},
isbn={978-989-758-760-3},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - SECRYPT
TI - From Real to Synthetic: GAN and DPGAN for Privacy Preserving Classifications
SN - 978-989-758-760-3
IS - 2184-7711
AU - Emadi, M.
AU - Moghtadaiee, V.
AU - Alishahi, M.
PY - 2025
SP - 711
EP - 716
DO - 10.5220/0013566000003979
PB - SciTePress

- Science and Technology Publications, Lda.
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