Research on Privacy Protection of Digital Twin Intelligence Based on Big Data in 5G System Security

Abstract

Digital twin (DT) technology is becoming a foundational component of next-generation 5G and 6G networks, enabling real-time monitoring, predictive analytics, and collaborative research. However, integrating DTs with big data and AI introduces significant privacy, security, and governance challenges. This article presents a privacy-preserving DT intelligence framework that integrates federated learning (FL) and differential privacy (DP) within a secure, standards-based architecture. The design follows ISO/IEC 27001 for information security management, complies with EU GDPR principles for data minimization and consent, and aligns with 3GPP TS 33.501 security procedures for 5G networks. The architecture combines TLS-secured data ingestion, blockchain-backed audit trails, and policy-driven compliance monitoring to ensure trusted DT operations without centralizing sensitive data. By mapping key threat vectors to corresponding international standards, the framework offers a practical blueprint for secure and interoperable DT deployments. The article concludes with deployment considerations and a roadmap for extending existing standards to support AI-native digital twins in future 6G environments.