This architecture significantly reduces network load, alleviates communication pressure, decreases data transmission costs, and enhances response speed--all critical factors for industrial control applications requiring high real-time performance [10; 11; 12]. However, the distributed nature of cloud-edge collaboration ICSs introduces substantial security and privacy challenges, particularly for edge nodes that serve as critical bridges between cloud platforms and industrial devices. Edge nodes process sensitive operational data while directly controlling physical processes, making them attractive targets for various attacks that can lead to industrial espionage, operational disruptions, and even catastrophic system failures. Among various defense mechanisms, the dynamic heterogeneous redundancy (DHR) architecture has emerged as a particularly promising response mechanism, offering sophisticated defensive capabilities through the deployment of functionally equivalent yet implementation-diverse heterogeneous executors [13]. DHR architecture addresses security vulnerabilities by transforming attacks into differential-mode or common-mode problems, where heterogeneous executors either produce varying outputs due to implementation differences or experience simultaneous impacts from sophisticated attacks. However, the application of DHR architectures in cloud-edge collaboration ICS environments presents unique challenges that current research has not adequately addressed. First, existing DHR implementations predominantly adopt event-triggered scheduling approaches, which can amplify the impact of common-mode escape scenarios in cloud-edge ICS deployments characterized by insufficient heterogeneity levels, potentially resulting in persistent attacker control over edge nodes. Second, current DHR research inadequately addresses privacy-preserving arbitration methodologies, limiting the ability of DHR architectures to fulfill the stringent privacy protection requirements inherent in cloud-edge collaboration ICS applications, where sensitive industrial data processing and cross-domain information sharing demand sophisticated privacy-preserving mechanisms beyond conventional arbitration approaches. To address these critical limitations, this paper proposes a novel privacy-preserving polymorphic heterogeneous security architecture specifically designed for cloud-edge collaboration ICSs. The main contributions of this work are as follows:  
* A privacy-preserving polymorphic heterogeneous security architecture for cloud-edge collaboration ICSs is proposed. Based on the DHR architecture, this framework integrates privacy-preserving mechanisms and closed-loop feedback control to address hardware and software homogeneity, singularity, and vulnerabilities in industrial control systems. * Based on the proposed architecture, this paper presents a novel privacy-preserving arbitration mechanism that dynamically balances privacy protection with operational efficiency. The scheme introduces a privacy budget parameter that adaptively adjusts the proportion of protected output bits based on real-time threat assessment and arbitration error rates, enabling edge-level preliminary consistency checking while maintaining cloud-level verification capabilities. This adaptive approach leverages the natural advantages of cloud-edge collaboration to ensure data confidentiality while preserving functional correctness and system availability in multi-executor environments. * To address the challenges of limited heterogeneous resources and underlying homogeneous risks in cloud-edge collaboration ICSs, this paper introduces credibility and heterogeneity metrics and designs a hybrid event-time triggered scheduling strategy based on these metrics for executor management. Simulation-based validation conducted on a natural gas pipeline pressure control system demonstrates that the proposed security architecture significantly strengthens system resilience against false data injection and privacy attacks, thereby confirming the effectiveness of our methodology.