Anonymization is a big problem when handling Industrial Web of Things (IIoT) data. Machine Learning (ML) applications require decrypted data to perform tasks efficiently, which implies that third parties involved in data processing can have access to sensitive information. This poses a risk of privacy leaks and knowledge leakage for the businesses generating the info. Consequently, attributable to these concerns, firms are hesitant to share their IIoT data with third parties.

The cutting-edge in addressing the anonymization problem involves various approaches akin to encryption, homomorphic encryption, cryptographic techniques, and distributed/federated learning. Nevertheless, these methods have limitations when it comes to computational costs, explainability of ML models, and vulnerabilities to cyber-attacks. Moreover, existing privacy preservation techniques often end in a trade-off between privacy and accuracy, where achieving high privacy protection results in a big loss in ML model accuracy. These challenges hinder the effective and efficient preservation of IIoT data privacy.

On this context, a research team from Kadir Has University in Turkey proposed a novel method that mixes Generative Adversarial Networks (GAN) and Differential Privacy (DP) to preserve sensitive data in IIoT operations. The hybrid approach goals to attain privacy preservation with minimal accuracy loss and low additional computational costs. The GAN is used to generate synthetic copies of sensitive data, while DP introduces random noise and parameters to take care of privacy. The proposed method is tested using publicly available datasets and a practical IIoT dataset collected from a confectionery production process.

The authors propose a hybrid privacy-preserving approach for IIoT environments. Their method involves two predominant components: GAN and DP.

1. GAN: They use GAN, specifically the Conditional Tabular GAN (CTGAN) approach, to create an artificial copy (XG) of the unique data set (XO). GAN learns the distribution of the info and generates synthetic data with similar statistics to the unique.
2. DP: To reinforce privacy, they add random noise from a Laplace distribution to sensitive features in the info. This method preserves privacy while maintaining the general probability distribution of the info.

The proposed approach involves the next:

• Creating an artificial data set with GAN.
• Replacing sensitive features.
• Applying differential privacy by adding random noise.

The resulting data set is privacy-preserving and might be used for machine learning evaluation without compromising sensitive information. The algorithm’s complexity relies on the variety of sensitive features and the scale of the info set. The authors emphasize that their method ensures overall privacy protection for IIoT data.

The evaluation performed on this paper involved conducting experiments to check the proposed hybrid approach for privacy-preserving data synthesis and prediction. The experiments were done on 4 SCADA data sets: wind turbine, steam production, energy efficiency, and synchronous motors. The experiments used the CTGAN synthetic data generation and differential privacy (DP) techniques. The evaluation criteria included measuring accuracy using the R-squared metric and privacy preservation using six privacy metrics. The outcomes showed that the proposed hybrid approach achieved higher accuracy and privacy preservation than other methods, akin to CTGAN and DP. The experiments also tested the performance of the proposed method on data sets with hidden sensitive features and demonstrated its ability to guard such sensitive data.

In conclusion, the paper proposed a novel hybrid approach combining GAN and DP to handle the anonymization problem in Industrial Web of Things (IIoT) data. The proposed method involves creating an artificial data set using GAN and applying DP by adding random noise to sensitive features. The evaluation results demonstrated that the proposed hybrid approach achieved higher accuracy and privacy preservation than other methods. This approach offers a promising solution for preserving sensitive data in IIoT environments while minimizing accuracy loss and computational costs.

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