Secure computation offloading for data leakage prevention in digital twin edge networks

Abstract Secure computation offloading is challenging because sensitive data can be exposed to malicious users, especially when the risk of data leakage arises during transmission or processing in untrusted edge environments. Therefore, in this paper, we present a novel content-aware data leakage prevention framework that classifies computational tasks into low, medium, and high sensitivity levels. We propose to address the trade-off between energy consumption and privacy under time constraints by integrating digital twin technology for dynamic and real-time network control. Compared with traditional edge computing and IoT environments, digital twin networks provide a virtualized, continuously synchronized representation of physical entities and enables more precise, context-aware decision-making for task offloading and security management. This bidirectional mapping between the physical and virtual worlds allows for proactive risk detection, adaptive privacy control, and optimized resource allocation, which are not achievable in conventional edge or IoT systems. In this regard, we utilize the digital twin’s real-time, synchronized representation of physical entities to design an energy and privacy-aware task scheduling scheme and minimize data leakage risks in digital twin edge networks. To achieve these, we analytically model both energy consumption and task processing delay using the M/M/1 queuing model. Furthermore, we propose an energy and privacy-aware genetic algorithm-based task scheduling algorithm designed to minimize energy consumption and latency while maximizing privacy in digital twin edge networks. Under strict latency constraints, numerical results show that while the conventional approach can handle up to 400 tasks, the proposed model scales efficiently to 600 tasks by prioritizing energy efficiency and privacy, and improves scalability by 1.5 times. Here, increasing the number of iterations has a much more positive effect than increasing the number of chromosomes. It achieves at most 84% privacy gain and acceptable 0.197 watts energy consumption in the topology with 0.58 second response time. In addition, the proposed approach offers the ability to further improve processing time and privacy gains through digital twin monitoring.