Work

Multidimensional Heterogeneous Hierarchical Measurement Model for Civil Aviation Passengers’ Sensitive Data

Year: 2026

Type: article

Abstract: To address the challenges of complex, heterogeneous, and blurred sensitivity boundaries in the sensitive data sources of civil aviation passengers, this paper proposes a hierarchical measurement method. This model integrates information entropy and random forest, achieving measurable sensitivity. Firstly, the correlation between data sensitivity level and business characteristics is established. Then, a Random Forest-based Hierarchical Measurement with Sensitivity Information Content Analysis (RF-HM-SICA) model integrating information entropy and random forest is proposed to construct a sensitivity measurable hierarchical measurement method for passenger sensitive data. The experimental results show that the RF-HM-SICA model exhibits high stability, generalization capability, and boundary sample protection ability under different data sizes and sensitivity levels, making it suitable for solving the multidimensional heterogeneity measurement problem of sensitive data of civil aviation passengers and providing support for data security sharing protection. In particular, the recognition accuracy and precision for high-sensitivity data approach 1.0 across datasets of different scales, while RF-HM-SICA exhibits the lowest misclassification rate among all compared models.

Source: Symmetry

DOI: 10.3390/sym18050738

Authors: Shuang Wang, Fangzheng Liu, Zhiping Li, Lei Ding, Zhaojun Gu

Institutions: Civil Aviation University of China, Tsinghua University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities