Anonymization is a problem when handling Industrial Internet of Things (IIoT) data.
This paper proposes a novel hybrid approach combining GAN and DP to address the anonymization problem in Industrial IoT of Things data.
The GAN is used to generate synthetic copies of sensitive data, while DP introduces random noise and parameters to maintain privacy.
Users may have many privacy concerns due to these concerns, and consequently, they are hesitant to share their IIoT data with third parties.
This challenges hinder the effective and efficient preservation of IIOT data privacy.
In conclusion, the paper proposed a novel combination of GANs and DP’s to address this issue.
It uses a weighted average generator to gain access to information and a diffusion analyzer to analyze its value
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