.png){kind=icon}
返回
Spatiotemporal Mobility Based Trajectory Privacy-Preserving Algorithm in Location-Based Services
.png){kind=icon}
OA
AI
DOI：10.3390/s21062021.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user's trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user's trajectory privacy. However, the user's attributes are rarely considered when constructing the k-anonymity set. It results in that the user's trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user's attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user's attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.
Keyword:
location-based services
trajectory privacy
trajectory data publishing
k-anonymity
spatiotemporal mobility
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
A trajectory privacy-preserving scheme based on query exchange in mobile social networks.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Trajectory privacy protection method based on the time interval divided基于时间间隔划分的轨迹隐私保护方法COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey隐私保护数据发布的匿名技术: 综合调查IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容