Secure data transmission and classification for digital twin

Secure data transmission and classification for digital twin

연구 분야: Analysis

학회: Science China Information Sciences

In Industry 4.0, digital twin (DT) technology plays an increasingly vital role in enabling intelligent and automated manufacturing and management. However, the utilization of DT in Industry 4.0 environments raises significant security concerns, particularly regarding data transmission and protection. This underscores the critical need for comprehensive and robust security frameworks specifically designed for data transmission and classification in DT-based systems. In this paper, we present a novel secure solution based on the purified Paillier cryptosystem to handle sensitive and categorical information through specialized verification keys and aggregation mechanisms. Our framework implements a three-layer architecture: the device layer uses trusted authority (TA) issued parameters to generate encrypted data types, content, and signatures; the edge layer employs verification keys to filter and aggregate required data types; and the DT layer performs final assessment and decryption. Additionally, we introduce an LSTM-RNN-based reverse data control strategy for DT network formulation and anomaly detection. Through extensive evaluation and testing, we demonstrate both the security robustness and performance efficiency of our proposed approach in realistic deployment scenarios.