[1]张俊,刘德安,申自浩,等.面向轨迹数据发布的KSDP方案[J].深圳大学学报理工版,2023,40(2):236-243.[doi:10.3724/SP.J.1249.2023.02236]
　ZHANG Jun,LIU Dean,SHEN Zihao,et al.KSDP scheme for trajectory data publishing[J].Journal of Shenzhen University Science and Engineering,2023,40(2):236-243.[doi:10.3724/SP.J.1249.2023.02236]
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面向轨迹数据发布的KSDP方案()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第40卷
期数:
2023年第2期
页码:
236-243
栏目:
电子与信息科学
出版日期:
2023-03-15

文章信息/Info

Title:
KSDP scheme for trajectory data publishing
文章编号:
202302014
作者:
张俊1;  刘德安1;  申自浩1;  王辉2;  刘沛骞2
1）河南理工大学计算机科学与技术学院，河南焦作 454000
2）河南理工大学软件学院，河南焦作 454000
Author(s):
ZHANG Jun1;  LIU Dean1;  SHEN Zihao1;  WANG Hui2;  and LIU Peiqian2
1) School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, Henan Province, P.R.China
2) School of Software, Henan Polytechnic University, Jiaozuo 454000, Henan Province, P.R.China
关键词:
数据安全与计算机安全; 轨迹隐私; 差分隐私; k-shape; 隐私保护; 数据发布
Keywords:
data security and computer security;  trajectory privacy;  differential privacy;  k-shape;  privacy protection;  data publishing
分类号:
TP309
DOI:
10.3724/SP.J.1249.2023.02236
文献标志码:
A
摘要:
轨迹隐私保护中使用k-means算法进行聚类时，对初始值敏感，且聚簇数目的选择具有一定的盲目性，为解决该问题并提高聚类结果的可用性，提出一种结合k-shape和差分隐私的轨迹隐私保护方案KSDP（k-shape differential privacy）．首先，对轨迹数据进行划分切割预处理，利用轨迹的时间属性和空间属性对轨迹切割划分，从而提高聚类泛化的质量．其次，使用设定的效用函数对预处理后的轨迹数据进行评判，并对过滤后数据进行聚类泛化操作．最后，在泛化后的数据中加入Laplace噪声，使其满足差分隐私保护模型，进一步保护轨迹隐私．实验仿真结果表明，与传统差分隐私k-means聚类方案对比，KSDP方案有效提高了聚类结果的可用性，并具有一定的性能优势，更好地实现了轨迹数据发布和隐私保护．
Abstract:
For clustering applications in the field of trajectory privacy protection, the k-means algorithm is sensitive to initial values and the number of clusters may be somewhat arbitrary. To address these issues and further improve the usability of clustering results, a trajectory privacy protection scheme combining k-shape and differential privacy (KSDP) is proposed. Firstly, the trajectory data is partitioned and preprocessed based on the temporal and spatial attributes of the trajectory to improve the quality of clustering generalization. Secondly, a utility function is used to evaluate the preprocessed trajectory data, and the clustering generalization is performed after filtering the data. Finally, Laplace noise is added to the generalized data to satisfy the differential privacy protection model, so as to further protect the trajectory privacy. The experimental simulation results show that compared with the traditional differential privacy k-means clustering scheme, the KSDP scheme effectively improves the availability of clustering results and achieves better trajectory data publishing and privacy protection.

参考文献/References:

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备注/Memo

备注/Memo:
Received: 2022- 10-11; Accepted: 2023-01-05; Online (CNKI): 2023-02-07
Foundation: National Natural Science Foundation of China (61300216); Key Scientific Research Projects of Colleges and Universities in Henan Province (23A520033); Doctoral Scientific Fund of Henan Polytechnic University (B2022-16, B2020-32)
Corresponding author: Associate professor SHEN Zihao. E-mail: szh@hpu.edu.cn
Citation: ZHANG Jun, LIU Dean, SHEN Zihao, et al. KSDP scheme for trajectory data publishing [J]. Journal of Shenzhen University Science and Engineering, 2023, 40(2): 236-243.(in Chinese)
基金项目：国家自然科学基金资助项目（61300216）；河南省高等学校重点科研资助项目（23A520033）；河南理工大学博士基金资助项目（B2022-16，B2020-32）
作者简介：张俊（1981—），河南理工大学硕士生导师、讲师．研究方向：隐私安全保护技术．E-mail:zhangjun@hpu.edu.cn
引文：张俊，刘德安，申自浩，等.面向轨迹数据发布的KSDP方案［J］.深圳大学学报理工版，2023，40（2）：236-243.
更新日期/Last Update: 2023-03-30