图书情报工作

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2025 , Vol. 69 >Issue 1: 46 - 57

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.01.005

研究论文

个人交通数据的敏感性识别与隐私计量研究

  • 臧国全 ,
  • 梁耀娣 ,
  • 柴文科 ,
  • 张盼盼 ,
  • 张恒苗
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  • 1 郑州大学信息管理学院, 郑州 450001;
    2郑州市数据科学研究中心, 郑州 450001
臧国全，教授，博士，博士生导师；梁耀娣，硕士研究生；柴文科，硕士研究生，通信作者，E-mail：chaiwenke2022@163.com；张盼盼，硕士研究生；张恒苗，本科生。

收稿日期: 2024-05-06

  修回日期: 2024-09-13

  网络出版日期: 2025-01-15

基金资助

本文系国家社会科学基金重大项目“政府数据的隐私风险计量与保护机制创新研究”(项目编号：21&ZD338)研究成果之一。

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Research on Sensitivity Identification and Privacy Measurement of Personal Traffic Data

  • Zang Guoquan ,
  • Liang Yaodi ,
  • Chai Wenke ,
  • Zhang Panpan ,
  • Zhang Hengmiao
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  • 1 School of Information Management, Zhengzhou University, Zhengzhou 450001;
    2 Research Institute of Data Science, Zhengzhou City, Zhengzhou 450001

Received date: 2024-05-06

  Revised date: 2024-09-13

  Online published: 2025-01-15

Supported by

This work is supported by the major project of the National Social science Fund of China, titled “Innovative research on privacy risk measurement and protection mechanism of government data” (Grant No. 21&ZD338).

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摘要

[目的/意义] 个人交通数据虽然根据数据泄露或非法使用对个人权益造成的危害程度划分为三个级别，但目前缺失具体的定量分级标准和明确的分级结果，本研究识别交通敏感数据项，计量个人交通数据隐私值，为其数据分级提供理论依据。[方法/过程] 首先，归纳交通隐私文本类型，构建隐私文本库作为识别来源；其次，挖掘文本库，解析文本句法结构和语义要素，建立交通敏感词表作为识别模型；最后，分析影响交通数据隐私的因素，建立隐私计量模型，计算交通敏感数据隐私值。[结果/结论] 研究表明，个人行踪轨迹数据和个人交通违法数据（交通违法记录、交通诚信记录）的隐私值最高，个人交通支付数据（运输服务购买与罚款支付、交通保险支付）和个人基础数据（标识数据、半标识数据、生物鉴别数据、网络鉴别数据、财产数据、健康数据）的隐私值次之，个人交通运营数据（客运票据、交通卡）的隐私值最低。

关键词： 交通数据; 数据隐私; 隐私识别; 隐私计量

本文引用格式

臧国全 , 梁耀娣 , 柴文科 , 张盼盼 , 张恒苗 . 个人交通数据的敏感性识别与隐私计量研究[J]. 图书情报工作, 2025 , 69(1) : 46 -57 . DOI: 10.13266/j.issn.0252-3116.2025.01.005

Abstract

[Purpose/Significance] Although personal traffic data is classified into three levels based on the degree to which data leakage or illegal use may harm individual rights and interests, there is currently a lack of specific quantitative basis and grading results. This study identifies personal traffic data items and measures the privacy value of personal traffic data, providing a theoretical basis for data grading. [Method/Process] Firstly, this paper classified traffic privacy text types and built a privacy text library as a source of identification. Secondly, it mined privacy text library, analyzed syntactic structure and language components, and established a traffic sensitive word list as a recognition model. Finally, it analyzed the factors affecting the privacy of traffic data, established a privacy measurement model, and measured the privacy value of traffic sensitive data. [Result/Conclusion] Research shows the ranking of personal traffic data privacy values from high to low is as follows. Personal track data and personal traffic violation data (traffic violation records, traffic integrity records) have the highest privacy value. Personal transportation payment data (transportation service purchase and fine payment, transportation insurance payment) and personal basic data (identification data, semi-identification data, biometric data, network identification data, property data, health data) have a secondary privacy value. Personal transportation operation data (passenger ticket, transportation card) have the lowest privacy value.

Key words： traffic data; data privacy; privacy identification; privacy measurement

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