Secure sharing model of data throughout the entire process of power equipment based on machine learning

Abstract

The distributed, heterogeneous, and shared security risks of power equipment data across its entire lifecycle limit the facilitation and integrated sharing of data across the entire lifecycle. This paper proposes a machine-learning-based secure data-sharing model for power equipment data during its lifecycle. The development of the proposed model includes multi-source data merging from the operation, inspection, and maintenance process into one data format through semantic mapping, in the unified structure of the data so that machine-learning is used for feature extraction and risk-prediction for dynamic access control and the adaptive encryption and de-sensitization balance between risk mitigation and data sharing is still maintained. The full-process monitoring and feedback monitoring to detect anomalous behavior can also optimize the polices in real-time. The experimentation provides an assigned data penetration rate of 96.3% for the disconnector. The leakage rate of sensitive information was reduced to 1.8% once the risk level was increased to extremely high. This separation alleviates the conflict of data security and data sharing by providing original research efforts for intelligent information and database systems.