Research Article
De-anonymization Attack Method of Mobility Trajectory Data Based on Semantic Trajectory Pattern
@INPROCEEDINGS{10.1007/978-3-030-89814-4_26, author={Wenshuai Zhang and Weidong Yang and Haojun Zhang and Zhenqiang Xu}, title={De-anonymization Attack Method of Mobility Trajectory Data Based on Semantic Trajectory Pattern}, proceedings={Mobile Multimedia Communications. 14th EAI International Conference, Mobimedia 2021, Virtual Event, July 23-25, 2021, Proceedings}, proceedings_a={MOBIMEDIA}, year={2021}, month={11}, keywords={Privacy protection De-anonymization attack Frequent pattern mining}, doi={10.1007/978-3-030-89814-4_26} }- Wenshuai Zhang
Weidong Yang
Haojun Zhang
Zhenqiang Xu
Year: 2021
De-anonymization Attack Method of Mobility Trajectory Data Based on Semantic Trajectory Pattern
MOBIMEDIA
Springer
DOI: 10.1007/978-3-030-89814-4_26
Abstract
Anonymizing trajectory data based on pseudonyms is a common privacy protection method in data publishing scenarios. The so-called de-anonymization attack associates the anonymized trajectory data with the real identity of mobile object to further obtain the private information. The trajectory of a mobile object contains detailed spatio-temporal and semantic information. For anonymously released trajectory data, we propose a de-anonymization attack method based on semantic trajectory patterns, which uses a semantic trajectory pattern acquisition algorithm to obtain the frequent semantic trajectory pattern set of each mobile object, which is used as trajectory features to construct its mobility profiles, further design the corresponding similarity measure. Experiments on real trajectory datasets show that the method proposed in this paper can obtain a relatively high de-anonymization success rate.
