LU Qiwei, WANG Caimei, XIONG Yan, XIA Huihua, HUANG Wenchao, GONG Xudong. Personalized Privacy-Preserving Trajectory Data Publishing[J]. Chinese Journal of Electronics, 2017, 26(2): 285-291. doi: 10.1049/cje.2017.01.024
Citation: LU Qiwei, WANG Caimei, XIONG Yan, XIA Huihua, HUANG Wenchao, GONG Xudong. Personalized Privacy-Preserving Trajectory Data Publishing[J]. Chinese Journal of Electronics, 2017, 26(2): 285-291. doi: 10.1049/cje.2017.01.024

Personalized Privacy-Preserving Trajectory Data Publishing

doi: 10.1049/cje.2017.01.024
Funds:  This work is supported by the National Natural Science Foundation of China (No.61202404, No.61170233, No.61232018, No.61272472, No.61272317), and the Fundamental Research Funds for the Central Universities (No.WK0110000041).
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  • Corresponding author: WANG Caimei (corresponding author) was born in 1978. She is a lecturer in Department of Computer Science and Technology, HeFei University. Her main research interests include computer network, information security, and mobile computation. (Email:wangcmo@mail.ustc.edu.cn)
  • Received Date: 2014-12-30
  • Rev Recd Date: 2015-01-28
  • Publish Date: 2017-03-10
  • Due to the popularity of mobile internet and location-aware devices, there is an explosion of location and trajectory data of moving objects. A few proposals have been proposed for privacy preserving trajectory data publishing, and most of them assume the attacks with the same adversarial background knowledge. In practice, different users have different privacy requirements. Such non-personalized privacy assumption does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. We study the personalized trajectory k-anonymity criterion for trajectory data publication. Specifically, we explore and propose an overall framework which provides privacy preserving services based on users' personal privacy requests, including trajectory clustering, editing and publication. We demonstrate the efficiency and effectiveness of our scheme through experiments on real world dataset.
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