基于历史查询概率的K-匿名哑元位置选取算法

doi:10.19678/j.issn.1000-3428.0060417

计算机工程 ›› 2022, Vol. 48 ›› Issue (2): 147-155. doi: 10.19678/j.issn.1000-3428.0060417

基于历史查询概率的K-匿名哑元位置选取算法

杨洋, 胡晓辉, 杜永文

兰州交通大学电子与信息工程学院, 兰州 730070

收稿日期:2020-12-28 修回日期:2021-02-25 发布日期:2021-03-22
作者简介:杨洋(1994-),女,硕士研究生,主研方向为位置隐私保护;胡晓辉,教授、博士;杜永文,副教授、博士。
基金资助:
国家自然科学基金（11461038，61163009）；甘肃省高等学校创新基金（2020A-033）；甘肃省科技支撑计划项目（144NKCA040）。

The K-Anonymous Dummy Location Selection Algorithm Based on Historical Query Probability

YANG Yang, HU Xiaohui, DU Yongwen

School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2020-12-28 Revised:2021-02-25 Published:2021-03-22

摘要/Abstract

摘要： 基于历史查询概率的哑元位置隐私保护机制存在匿名度低、隐匿区域小和位置分布不均匀的问题。提出K-匿名哑元位置选取（K-DLS）算法用于位置隐私保护。通过综合考虑匿名集的位置离散度和零查询用户，增强哑元匿名集的隐私性。利用熵度量选择哑元位置，使得哑元匿名集的熵值最优，并根据位置偏移距离优化匿名结果，增加匿名集的位置离散度。仿真结果表明，K-DLS算法的哑元匿名集离散度优于DLS、DLP、Enhanced_DLP等算法，能够有效提高用户位置的隐私保护效果。

关键词: 基于位置的服务, 位置隐私, 哑元位置选取, 零查询用户, K-匿名, 地理位置分布

Abstract: The dummy-based location privacy mechanism using historical query probability suffers from low anonymity, small coverage area and imbalanced location distribution.To address the problem, a K-anonymous dummy-based locationselection algorithm is proposed for position privacy protection.The privacy of dummy anonymous set is enhanced by comprehensively considering the location dispersion of anonymous set and zero-query users.The algorithm selects the location of dummy through entropymeasure to make the entropy of the anonymous dummy set optimal.Then the anonymous result is optimized based on the offset distance of the location, and the location dispersion of the constructed anonymous set is improved.The simulation results show that the proposed algorithm displays a higher location dispersion degree of the dummy-based anonymous set than DLS, DLP, Enhanced_DLP and other algorithms.It significantly improves the performance of location privacy protection for users.

Key words: Location Based Service(LBS), location privacy, dummy location selection, zero-query users, K-anonymity, geographic distribution of locations

中图分类号:

TP309

杨洋, 胡晓辉, 杜永文. 基于历史查询概率的K-匿名哑元位置选取算法[J]. 计算机工程, 2022, 48(2): 147-155.

YANG Yang, HU Xiaohui, DU Yongwen. The K-Anonymous Dummy Location Selection Algorithm Based on Historical Query Probability[J]. Computer Engineering, 2022, 48(2): 147-155.

http://www.ecice06.com/CN/Y2022/V48/I2/147

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