Data grading for intelligent connected vehicles: an inference strength-driven framework

Abstract

Intelligent Connected Vehicles (ICVs) generate massive heterogeneous multi-modal data during operation, and due to the limited computing resources on board, graded data encryption protection is of great significance for balancing data security and efficient utilization. However, the current data grading processes struggle to address the evolving inference attacks and dynamic operational environments, and traditional grading approaches relying on static expert judgment or information-theoretic metrics. To bridge this gap, this paper proposes a novel inference strength-driven data grading framework, where inference strength quantifies the susceptibility of one dataset to infer another through adversarial reasoning. The framework employs a systematic methodology combining graph theory, optimization, and Large Language Models to construct an inference library and calculate inference strength. The framework also provides a PageRank-based algorithm to generate interpretable data grading lists for both static policy and vehicle-end application, prioritizing core data protection while respecting computational constraints. Validated on the Audi A2D2 dataset and real vehicle controller, our approach demonstrates improved protection utility compared to default grading baselines. The results highlight its potential to enhance data security in ICVs through prioritized protection of core data under computational constraints.