On the Private Data Synthesis Through Deep Generative Models for Data Scarcity of Industrial Internet of Things

Yen Ting Chen, Chia Yi Hsu, Chia Mu Yu*, Mahmoud Barhamgi, Charith Perera

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the data-driven intelligence from the recent deep learning based approaches, the huge amount of data collected from various kinds of sensors from industrial devices have the potential to revolutionize the current technologies used in the industry. To improve the efficiency and quality of machines, the machine manufacturer needs to acquire the history of the machine operation process. However, due to the business secrecy, the factories are not willing to do so. One promising solution to the abovementioned difficulty is the synthetic dataset and an informatic network structure, both through deep generative models such as differentially private generative adversarial networks. Hence, this article initiates the study of the utility difference between the abovementioned two kinds. We carry out an empirical study and find that the classifier generated by private informatic network structure is more accurate than the classifier generated by private synthetic data, with approximately 0.31-7.66%.

Original languageEnglish
Pages (from-to)551-560
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume19
Issue number1
DOIs
StatePublished - 1 Jan 2023

Keywords

  • Data synthesis
  • deep generative model (DGM)
  • differential privacy (DP)
  • generative adversarial network (GAN)
  • industrial Internet of Things (IIoT)

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