Spatio-temporal de-anonymization attack on geolocated data

被引：0

作者：

Wang R. ^{[1
]}

Xie W. ^{[1
]}

Liao X. ^{[1
]}

Feng S. ^{[1
]}

Bai K. ^{[2
]}

机构：

[1] Industrial Cybersecurity Business Department, Institute of Security Research, China Academy of Information and Communications Technology, Beijing

[2] Laboratory of Trusted Computing and Information Assurance, Institute of Software, Chinese Academy of Sciences, Beijing

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2021年 / 42卷 / 03期

关键词：

De-anonymization; De-anonymization attack; Hidden Markov model; Location privacy; Markov model; Privacy; Spatio-temporal influence; User reidentification;

D O I：

10.11990/jheu.201912060

中图分类号：

学科分类号：

摘要：

De-anonymitzation technology based on the spatial attribute of the user's moving track does not make full use of the time attribute of the moving track, so the accuracy of re-recognition requires further improvement. In this paper, we first quantitatively analyze spatial and temporal attributes contained in the moving track, define a spatiotemporal-sensed user hidden Markov model to describe the user's moving behavior, and further present a spatiotemporal-sensed de-anonymization attack, defining two Spatio-temporal similarities for the attack model. The experiment was carried out on the dataset named GeoLife collected by the Microsoft Asia Research Institute. The experimental results show that the proposed STCS model improves reidentification accuracy significantly compared with previous STES models. Copyright ©2021 Journal of Harbin Engineering University.

引用

页码：400 / 406

页数：6

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