基于语义融合轨迹生成的k匿名轨迹集补全方法

doi:10.3969/j.issn.1671-1122.2024.12.009

信息网络安全 ›› 2024, Vol. 24 ›› Issue (12): 1911-1921.doi: 10.3969/j.issn.1671-1122.2024.12.009

基于语义融合轨迹生成的k匿名轨迹集补全方法

徐健锋¹, 张炜¹, 涂敏²^,³(), 魏勍颋¹, 赖展晴¹, 王倩倩¹

1.南昌大学软件学院，南昌 330031
2.江西警察学院网络安全学院，南昌 330103
3.电子数据管控与取证江西省重点实验室，南昌 330103

收稿日期:2024-06-11 出版日期:2024-12-10 发布日期:2025-01-10
通讯作者: 涂敏 tumin_y@163.com
作者简介:徐健锋（1973—），男，江西，教授，博士，CCF会员，主要研究方向为人工智能、网络空间安全、软件工程、粒计算理论、粗糙集理论、三支决策|张炜（2001—），男，江西，硕士研究生，主要研究方向为网络安全|涂敏（1967—），女，江西，教授，本科，主要研究方向为网络安全、电子数据取证|魏勍颋（1981—），女，江西，讲师，博士，CCF会员，主要研究方向为智能计算、信息安全、生物信息学|赖展晴（1999—），男，江西，硕士研究生，主要研究方向为网络安全|王倩倩（2004—），女，江西，本科，主要研究方向为网络安全
基金资助:
国家自然科学基金(62266032);国家自然科学基金(62362050);江西省主要学科学术技术带头人领军人才项目(20225BCI22016);江西省教育厅2022年科学技术研究项目(GJJ2202302)

A k-Anonymity Completion Method Generated Based on Semantic Fusion Trajectories

XU Jianfeng¹, ZHANG Wei¹, TU Min²^,³(), WEI Qingting¹, LAI Zhanqing¹, WANG Qianqian¹

1. School of Software, Nanchang University, Nanchang 330031, China
2. Jiangxi Police Academy Network Security College, Nanchang 330103, China
3. Key Laboratory of Electronic Data Control and Evidence Collection, Nanchang 330103, China

Received:2024-06-11 Online:2024-12-10 Published:2025-01-10

摘要/Abstract

摘要：

轨迹隐私保护是数据安全和个人隐私保护领域的热点问题之一。文章针对k匿名轨迹计算中可能存在匿名轨迹数量不足的问题，提出一种基于语义融合的匿名轨迹生成方法。该方法选择间距小于指定阈值且存在通路的轨迹对，进行融合和校准后生成两条具有较好语义解释性的虚拟轨迹。基于上述研究成果，文章进一步提出一种基于语义融合轨迹生成的k匿名轨迹集补全方法TS-ATC。该方法首先从匿名轨迹集中选取轨迹作为候选轨迹集；然后，从候选轨迹集中选取符合条件的轨迹对执行基于语义融合的匿名轨迹生成方法，并将符合条件的生成轨迹添加到匿名轨迹集。如果匿名轨迹集数量还达不到要求，再从k匿名轨迹计算淘汰的轨迹中选择合适的轨迹加入候选轨迹集，并进行轨迹融合生成。该步骤也将符合条件的生成轨迹再次添加入匿名轨迹集，直至匿名轨迹集的数量达到要求。文章提出的轨迹生成及匿名轨迹补全方法不但具有较好的可解释性，同时能够有效解决k匿名轨迹计算中可能遇到的轨迹数量不足的问题。

关键词: 隐私保护, 轨迹补全, 轨迹生成, 语义融合

Abstract:

Trajectory privacy protection is one of the hot issues in the field of data security and personal privacy protection. Aiming at the problem that the number of anonymous trajectories might be insufficient in k-anonymous trajectory computation, the article proposed an anonymous trajectory generation method based on semantic fusion. The method selected pairs of trajectories with spacing less than a specified threshold and with pathways, and generates two virtual trajectories with better semantic interpretations after fusion and calibration. Based on the above research results, the article further proposed an anonymous trajectory set complementation algorithm based on semantic fusion trajectory generation. The method first selected trajectories from the anonymous trajectory set as the candidate trajectory set; then, the eligible trajectory pairs were selected from the candidate trajectory set to execute the semantic fusion-based anonymous trajectory generation method, and the eligible generated trajectories were added into the anonymous trajectory set. If the number of anonymous trajectory sets was still not enough to meet the requirements, suitable trajectories could also be selected again from the trajectories eliminated by the k-anonymous trajectory computation to be added to the candidate trajectory set, and the trajectory fusion generation could be performed again. This step also added the eligible generated trajectories into the anonymous trajectory set again until the number of anonymous trajectory sets reached the requirement. The trajectory generation and anonymous trajectory complementation method proposed in the article not only has good interpretability, but also can effectively solve the problem of insufficient number of trajectories that may be encountered in k-anonymous trajectory computation.

Key words: privacy protection, trajectory completion, trajectory generation, semantic fusion

中图分类号:

TP309

徐健锋, 张炜, 涂敏, 魏勍颋, 赖展晴, 王倩倩. 基于语义融合轨迹生成的k匿名轨迹集补全方法[J]. 信息网络安全, 2024, 24(12): 1911-1921.

XU Jianfeng, ZHANG Wei, TU Min, WEI Qingting, LAI Zhanqing, WANG Qianqian. A k-Anonymity Completion Method Generated Based on Semantic Fusion Trajectories[J]. Netinfo Security, 2024, 24(12): 1911-1921.

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基于语义融合轨迹生成的k匿名轨迹集补全方法

A k-Anonymity Completion Method Generated Based on Semantic Fusion Trajectories

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