Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces significant challenges related to data management, regulatory compliance, and user privacy. In scenarios where selective data removal is necessary, such as device deactivation, network reconfiguration, or regulatory compliance, traditional approaches often fall short of preserving the integrity of the twin model. To address this gap, we introduce a network digital untwinning framework that enables the targeted removal of deprecated NDT contributions while maintaining model integrity. Our approach comprises two complementary components: Single Request Untwinning (\algO) and Parallel Request Untwinning (\algM) mechanisms. \algO leverages connectivity metrics based on geographical proximity, data distribution, and network-level attributes to identify and remove the target NDT along with its propagating influence. This is achieved through an optimally selected rollback checkpoint augmented with injected Gaussian noise, followed by a precise remapping phase. \algM extends this mechanism to efficiently handle multiple removal requests by clustering NDTs with similar attributes and performing a coordinated rollback and untwinning schedule. We provide theoretical guarantees on model indistinguishability from scratch-built twins, and validate the framework through extensive experiments on real-world traffic data, demonstrating its effectiveness and operational efficiency.

翻译：网络数字孪生（NDTs）通过提供物理网络系统的精确虚拟副本，正在变革网络管理实践。然而，其对多样化敏感数据的依赖带来了数据管理、法规遵从与用户隐私方面的重大挑战。在需选择性数据移除的场景中（如设备停用、网络重构或法规合规要求），传统方法往往难以保持孪生模型的完整性。为填补这一空白，我们提出网络数字解孪生框架，可在保持模型完整性的前提下实现已弃用NDT贡献的定向移除。该框架包含两大互补组件：单请求解孪生（\algO）机制与并行请求解孪生（\algM）机制。\algO通过基于地理邻近性、数据分布及网络级属性的连通性度量，识别并移除目标NDT及其传播影响，其实现路径为：选择经高斯噪声注入优化的回滚检查点，随后执行精确重映射阶段。\algM将该机制扩展至多请求高效处理场景，通过聚合相似属性NDT并执行协同回滚与解孪生调度实现。我们提供了与从零构建孪生模型相比模型无区分性的理论保证，并在真实流量数据上通过大量实验验证了该框架的有效性与运行效率。