面向数据出境的多维融合动态评估模型与高效计算技术

A Multidimensional Fusion Dynamic Evaluation Model and Efficient Computing Technology for Data Cross-Border Transfer

摘要: 在数据跨境流动日趋频繁的现实背景下，敏感数据出境安全已成为维护国家安全、公共利益与个人信息合法权益的关键环节。当前主流的数据出境风险评估方法多依赖静态指标体系与线性加权模型，存在风险要素耦合刻画不足、动态适配能力较弱、大规模场景下计算效率偏低等问题，难以满足金融、医疗、交通、工业等重点行业对风险精准量化、实时研判的实际需求。本文面向敏感数据出境全生命周期风险管控需求，提出一种多维融合动态评估模型。该模型构建覆盖数据敏感性、出境必要性、传输安全、主体管理能力与境外接收方保障水平的多维度风险指标体系，通过自注意力机制实现风险指标权重的动态分配，引入非线性耦合函数刻画多要素间风险叠加与传导效应，并依托在线学习实现模型参数的自适应更新。本文首次将自注意力机制与非线性耦合函数结合用于数据出境风险的动态量化评估，该模型能够更好地适应敏感数据出境风险的动态演变特征，具备良好的场景适应性与计算高效性，可以为机构数据出境合规自查和风险监测提供技术支持。

Abstract: Against the backdrop of increasingly frequent cross-border data flows, the security of sensitive data outbound transfer has become a critical link in safeguarding national security, public interests and the legitimate rights and interests of personal information. Most mainstream risk assessment methodologies for data outbound transfer currently rely on static indicator systems and linear weighting models. Such approaches suffer from insufficient depiction of the coupling between risk factors, weak dynamic adaptability, and low computational efficiency in large-scale scenarios, making them unable to meet the practical demands of key sectors including finance, healthcare, transportation and industry for accurate risk quantification and real-time risk analysis. Targeting the full-lifecycle risk control requirements for sensitive data outbound transfer, this paper proposes a multi-dimensional integrated dynamic assessment model. The model establishes a multi-dimensional risk indicator system covering data sensitivity, necessity of outbound transfer, transmission security, management capacity of data controllers, and the protection standard of overseas data recipients. It leverages the self-attention mechanism to dynamically assign weights to risk indicators, introduces a nonlinear coupling function to characterize the risk superposition and transmission effects among multiple factors, and realizes adaptive updates of model parameters via online learning. This paper innovatively combines the self-attention mechanism and nonlinear coupling function for the dynamic quantitative assessment of data outbound transfer risks for the first time. Capable of better adapting to the dynamic evolution characteristics of sensitive data outbound risks, the proposed model boasts favorable scenario adaptability and high computational efficiency. It can provide technical support for organizations to conduct compliance self-inspections and risk monitoring of cross-border data transfers.