Cross-regional data protection: a synergistic framework integrating encryption, access control, and privacy-enhancing technologies

Cross-regional ecosystems generate vast volumes of sensitive data, including personal records, activity logs, and organizational resources, which are increasingly critical for secure collaboration and data-driven decision-making. However, distributed storage and frequent inter-organizational exchanges heighten risks of unauthorized access and privacy breaches. Existing solutions, encryption, access control, and privacy-enhancing technologies (PETs), have been studied extensively, but remain fragmented and insufficient to address the scalability and heterogeneity of large-scale deployments. To bridge this gap, this study proposes a synergistic defense framework integrating lightweight homomorphic encryption for secure data storage and transmission, multi-factor trust-based access control for dynamic authorization, and hybrid PETs such as differential privacy and federated learning for privacy-preserving analytics. The framework is formalized through mathematical models and implemented in a layered architecture spanning data, security, and application layers. Experimental evaluation on distributed datasets demonstrates significant improvements over encryption-only, access-control-only, and PETs-only baselines, achieving lower latency (10.4 ms), reduced leakage probability (2.1%), and higher accuracy retention (96.9%). Convergence, ablation, and robustness analyses further confirm its stability and interpretability. This research advances theoretical understanding of collaborative defense while offering a scalable, privacy-conscious infrastructure for sensitive data protection across diverse domains.