.png){kind=icon}
返回
Deep learning-based data privacy protection in software-defined industrial networking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2023.108578.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The industrial Internet connects equipment to the network and utilizes the data generated to assist businesses. Industrial big data is the result of data accumulation; thus, the industrial Internet has to adopt new technologies-namely, software-defined industrial networks (SDIN) -to keep up with these developments. This study suggests a deep differential privacy data protection algorithm based on SDIN. The deep learning model is analyzed and integrated with differential privacy to provide the process framework for the deep differential privacy data protection algorithm. The equivalent model of the generative adversarial network is used to allow the attacker to obtain the optimal fake samples. The balance between dataset availability and privacy protection is achieved by implementing parameter tuning on the deep differential privacy model. The experimental results show that the proposed algorithm has strong industrial data privacy protection and high data availability and can effectively guarantee the privacy security of industrial data.
Keyword:
Software -defined industrial networking
Deep learning
Data privacy protection
Differential privacy
Generative adversarial network
Convolutional neural networks
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
.png){kind=icon}
Diabetes, Cardiorenal, and Metabolic Multispecialty Practice Recommendations and Early Intensive Management of Cardio-Renal-Metabolic Disease糖尿病、心肾和代谢多专科实践建议及心-肾-代谢疾病的早期强化管理
PREAI
分享收藏An analysis of generative adversarial networks and variants for image synthesis on MNIST dataset
PREAI
分享收藏
查看更多内容