Paper Title :Privacy-Preserving Generative Adversarial Networks for Synthetic Data Generation in Resource-Constrained Environments
Author :Prabhakaran Selvaraj, Selvakuberan Karuppasamy
Article Citation :Prabhakaran Selvaraj ,Selvakuberan Karuppasamy ,
(2024 ) " Privacy-Preserving Generative Adversarial Networks for Synthetic Data Generation in Resource-Constrained Environments " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 1-5,
Volume-12,Issue-9
Abstract : Privacy-preserving Generative Adversarial Networks (GANs) have shown promise in generating synthetic data
while protecting individual privacy. However, deploying such models in resource-constrained environments, such as edge
devices or low-power systems, poses significant challenges. This paper proposes novel techniques for optimizing privacypreserving
GANs to operate efficiently in resource-constrained environments. Specifically, we explore techniques for model
compression, quantization, and decentralized training to enable the deployment of privacy preserving GANs on devices with
limited computational resources. We evaluate the performance and privacy guarantees of our proposed methods through
extensive experimentation on real-world datasets. Our research contributes to advancing the field of privacy-preserving
machine learning by enabling the widespread adoption of GANs in resource-constrained environments without
compromising privacy or performance.
Keywords - Generative Adversarial Networks, Edge Devices
Type : Research paper
Published : Volume-12,Issue-9
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21240
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Copyright: © Institute of Research and Journals
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Published on 2025-01-08 |
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