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

*此作品的通信作者

研究成果: Article同行評審

摘要

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%.

原文English
頁(從 - 到)551-560
頁數10
期刊IEEE Transactions on Industrial Informatics
19
發行號1
DOIs
出版狀態Published - 1 1月 2023

指紋

深入研究「On the Private Data Synthesis Through Deep Generative Models for Data Scarcity of Industrial Internet of Things」主題。共同形成了獨特的指紋。

引用此