Re-identification of Vehicular Location-Based Metadata
Amid the flourish of various data services, the privacy problems on metadata have received sufficient attention. Generally, the identity is the most sensitive attribute in metadata as identity is the key linking all attributes together. Thus, quite a few methods, such as dummy and k-anonymity, have...
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European Alliance for Innovation (EAI)
2017-12-01
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Online Access: | http://eudl.eu/doi/10.4108/eai.7-12-2017.153393 |
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doaj-fa573d9d117845c0a337ebd27497384b2020-11-25T01:28:34ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Security and Safety2032-93932017-12-0141111210.4108/eai.7-12-2017.153393Re-identification of Vehicular Location-Based MetadataZheng Tan0Cheng Wang1Xiaoling Fu2Jipeng Cui3Changjun Jiang4Weili Han5Tongji University, Shanghai 201804, China; 102456@tongji.edu.cnTongji University, Shanghai 201804, ChinaTongji University, Shanghai 201804, ChinaTongji University, Shanghai 201804, ChinaTongji University, Shanghai 201804, ChinaSoftware School, Fudan University, Shanghai 201203, ChinaAmid the flourish of various data services, the privacy problems on metadata have received sufficient attention. Generally, the identity is the most sensitive attribute in metadata as identity is the key linking all attributes together. Thus, quite a few methods, such as dummy and k-anonymity, have been applied to actual applications to protect the identity . However, we still argue that the identity is very likely to be disclosed. In this paper, we study the re-identification problem in the seemingly privacy-preserving VLBS (Vehicular Location-Based Service). We find that the trajectories of vehicles are highly unique after studying 131 millions mobility traces of taxis. More specifically, the experiments demonstrate that only four spatio-temporal points are sufficient to uniquely re-identify the vehicle, achieving an accuracy of 95.35%. This indicates that there exists a high risk of re-identification in VLBS even identity has been protected by traditional methods.http://eudl.eu/doi/10.4108/eai.7-12-2017.153393PrivacyVLBSRe-identificationUniquenessTrajectories |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zheng Tan Cheng Wang Xiaoling Fu Jipeng Cui Changjun Jiang Weili Han |
spellingShingle |
Zheng Tan Cheng Wang Xiaoling Fu Jipeng Cui Changjun Jiang Weili Han Re-identification of Vehicular Location-Based Metadata EAI Endorsed Transactions on Security and Safety Privacy VLBS Re-identification Uniqueness Trajectories |
author_facet |
Zheng Tan Cheng Wang Xiaoling Fu Jipeng Cui Changjun Jiang Weili Han |
author_sort |
Zheng Tan |
title |
Re-identification of Vehicular Location-Based Metadata |
title_short |
Re-identification of Vehicular Location-Based Metadata |
title_full |
Re-identification of Vehicular Location-Based Metadata |
title_fullStr |
Re-identification of Vehicular Location-Based Metadata |
title_full_unstemmed |
Re-identification of Vehicular Location-Based Metadata |
title_sort |
re-identification of vehicular location-based metadata |
publisher |
European Alliance for Innovation (EAI) |
series |
EAI Endorsed Transactions on Security and Safety |
issn |
2032-9393 |
publishDate |
2017-12-01 |
description |
Amid the flourish of various data services, the privacy problems on metadata have received sufficient attention. Generally, the identity is the most sensitive attribute in metadata as identity is the key linking all attributes together. Thus, quite a few methods, such as dummy and k-anonymity, have been applied to actual applications to protect the identity . However, we still argue that the identity is very likely to be disclosed. In this paper, we study the re-identification problem in the seemingly privacy-preserving VLBS (Vehicular Location-Based Service). We find that the trajectories of vehicles are highly unique after studying 131 millions mobility traces of taxis. More specifically, the experiments demonstrate that only four spatio-temporal points are sufficient to uniquely re-identify the vehicle, achieving an accuracy of 95.35%. This indicates that there exists a high risk of re-identification in VLBS even identity has been protected by traditional methods. |
topic |
Privacy VLBS Re-identification Uniqueness Trajectories |
url |
http://eudl.eu/doi/10.4108/eai.7-12-2017.153393 |
work_keys_str_mv |
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1725100739436478464 |