On the private data synthesis through deep generative models for data scarsity of industrial Internet of Things

Chen, Yen-Ting, Hsu, Chia-Yi, Yu, Chia-Mu, Barhamgi, Mahmoud and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2023. On the private data synthesis through deep generative models for data scarsity of industrial Internet of Things. IEEE Transactions on Industrial Informatics 19 (1) , pp. 551-560. 10.1109/TII.2021.3133625

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Abstract

Due to the data-driven intelligence from the recent deep learning (DL)-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 above difficulty is the synthetic dataset and an informatic network structure, both through differentially private GANs (DP-GANs). Hence, this paper initiates the study of the utility difference between the above 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\%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1551-3203
Date of First Compliant Deposit:	28 January 2022
Date of Acceptance:	9 December 2021
Last Modified:	07 Nov 2023 07:09
URI:	https://orca.cardiff.ac.uk/id/eprint/147061

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