LI Wenping, DENG Kun, YU Fahong. Feature-Based Trajectory Privacy Preserving via Low-Rank and Sparse Decomposition[J]. Chinese Journal of Electronics, 2018, 27(4): 746-755. doi: 10.1049/cje.2018.04.015
Citation: LI Wenping, DENG Kun, YU Fahong. Feature-Based Trajectory Privacy Preserving via Low-Rank and Sparse Decomposition[J]. Chinese Journal of Electronics, 2018, 27(4): 746-755. doi: 10.1049/cje.2018.04.015

Feature-Based Trajectory Privacy Preserving via Low-Rank and Sparse Decomposition

doi: 10.1049/cje.2018.04.015
Funds:  This work is supported by the Humanity and Social Science Youth Foundation of Ministry of Education of China (No.15YJCZH088, No.17YJCZH033), the Zhejiang Provincial Education Department Research Foundation of China (No.Y201636127), and the Zhejiang Provincial Natural Science Foundation of China (No.LY16F020027).
  • Received Date: 2017-08-12
  • Rev Recd Date: 2018-01-25
  • Publish Date: 2018-07-10
  • The disclosure of sensitive contents hidden in trajectories may jeopardize individuals' privacy security. The privacy preserving technologies on trajectories face the challenge of how to give consideration to the spatiotemporal structure of mobile data. Traditional trajectory privacy preserving tricks focus chiefly on partial structure, while ignoring global feature of trajectories. This research orientation tends to cause exorbitant distortion on the spatiotemporal structure of trajectories, which may give rise to low data utility. The moving behavior has itself innate sparseness feature. Thus a trajectory privacy preserving method based on feature maintaining is proposed by introducing the low-rank and sparse decomposition technique about large matrix. The proposed method iteratively decompose the feature matrix until the rank achieving stability to extract the primary feature and eliminate the private parts. The released trajectories are reconstructed by perturbing the final refined feature matrix with a series of low-rank components generated in the decomposition procedure. Experimental results on real-world dataset verified that the proposed method has low information loss on large-scale data.
  • loading
  • S. Ilarri, D. Stojanovic and C. Ray, "Semantic management of moving objects:A vision towards smart mobility", Expert Systems with Applications, Vol.42, No.3, pp.1418-1435, 2015.
    R. Ivanov, "Real-time GPS track simplification algorithm for outdoor navigation of visually impaired", Journal of Network and Computer Applications, Vol.35, No.2, pp.1559-1567, 2012.
    M. Ghasemzadeh, B.C.M. Fung, R. Chen, et al., "Anonymizing trajectory data for passenger flow analysis", Transportation Research Part C, Vol.39, No.2, pp.63-79, 2014.
    Y. Zheng, L. Zhang, X. Xie, et al., "Mining interesting locations and travel sequences from gps trajectories", Proceedings of the 18th International Conference on World Wide Web (WWW2009), Madrid, Spain, pp.791-800, 2009.
    K. Al-Hussaeni, B.C.M. Fung and W.K. Cheung, "Privacypreserving trajectory stream publishing", Data and Knowledge Engineering, Vol.94, No.11, pp.89-109, 2014.
    A. Monreale, G. Andrienko, N. Andrienko, et al., "Movement data anonymity through generalization", IEEE Transactions on Data Privacy, Vol.3, No.2, pp.91-121, 2010.
    M. Gruteser and X. Liu, "Protecting privacy in continuous location-tracking applications", IEEE Security and Privacy, Vol.2, No.2, pp.28-34, 2004.
    M. Terrovitis and N. Mamoulis, "Privacy preservation in the publication of trajectories", Proceedings of the Ninth International Conference on Mobile Data Management, Washington, DC, USA, pp.65-72, 2008.
    R. Chen, B.C.M. Fung, N. Mohammed, et al., "Privacypreserving trajectory data publishing by local suppression", Information Sciences, Vol.231, No.4, pp.83-97, 2013.
    E.G. Komishani, M. Abadi and F. Deldar, "PPTD:Preserving personalized privacy in trajectory data publishing by sensitive attribute generalization and trajectory local suppression", Knowledge-based Systems, Vol.94, No.2, pp.43-59, 2016.
    H. Kido, Y. Yanagisawa and T. Satoh, "An anonymous communication technique using dummies for location-based services", Proceedings of International Conference on Pervasive Services, Santorini, Greece, pp.88-97, 2005.
    P.R. Lei, W.C. Peng, I.J. Su, et al., "Dummy-based schemes for protecting movement trajectories", Journal of Information Science and Engineering, Vol.28, No.2, pp.335-350, 2012.
    T. Hara, A. Suzuki, M. Iwata, et al., "Dummy-based user location anonymization under real-world constraints", IEEE Access, Vol.4, No.2, pp.673-687, 2016.
    H. Shen, G.W. Bai, M. Yang, et al., "Protecting trajectory privacy:A user-centric analysis", Journal of Network and Computer Applications, Vol.82, No.4, pp.128-139, 2017.
    M.E. Nergiz, M. Atzori, Y. Saygin, et al., "Towards trajectory anonymization:A generalization-based approach", IEEE Transactions on Data Privacy, Vol.2, No.1, pp.47-75, 2009.
    T. Peng, Q. Liu, D. Meng, et al., "Collaborative trajectory privacy preserving scheme in location-based services", Information Sciences, Vol.387, No.4, pp.165-179, 2017.
    Q. Lu, C. Wang, Y. Xiong, et al., "Personalized PrivacyPreserving trajectory data publishing", Chinese Journal of Electronics, Vol.26, No.2, pp.285-291, 2017.
    Y. Xin, Z.Q. Xie and J. Yang, "The privacy preserving method for dynamic trajectory releasing based on adaptive clustering", Information Sciences, Vol.378, No.2, pp.131-143, 2017.
    J. Wright, A. Ganesh, S. Ral, et al., "Robust principal component analysis:Exact recovery of corrupted low-rank matrices", Journal of the ACM, Vol.58, No.3, pp.1-37, 2011.
    G. Liu, Z. Lin, S. Yan, et al., "Robust recovery of subspace structures by low-rank representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.1, pp.171-184, 2013.
    B. Cheng, G. Liu, J. Wang, et al., "Multi-task low-rank affinity pursuit for image segmentation", Proceedings of the Thirteenth IEEE International Conference on Computer Vision, Barcelona, Spain, pp.2439-2446, 2011.
    X. Yuan and J. Yang, "Sparse and low rank matrix decomposition via alternating direction method", Pacific Journal of Optimization, Vol.9, No.1, pp.1-11, 2009.
    M. Chen, A. Ganesh, Z. Lin, et al., "Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix", Journal of the Marine Biological Association of the UK, Vol.56, No.3, pp.707-722, 2009.
    Z. Lin, M. Chen and Y. Ma, "The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices", Eprint Arxiv, arXiv:1009.5055v3, DOI:10.1016/j.jsb.2012.10.010, 2013.
    Y. Zheng, Q. Li, Y. Chen, et al., "Understanding mobility based on GPS data", Proceedings of ACM Conference on Ubiquitous Computing, Seoul, Korea, pp.312-321, 2008.
    Z. Huo, Y. Huang and X. Meng, "History trajectory privacypreserving through graph partition", Proceedings of the 1st International Workshop on Mobile Location-based Service, Beijing, China, pp.71-78, 2011.
    S. Gao, J. Ma and C. Sun, "Balancing trajectory privacy and data utility using a personalized anonymization model", Journal of Network and Computer Applications, Vol.38, pp.125-134, 2014.
    M. Terrovitis, G. Poulis, N. Mamoulis, et al., "Local suppression and splitting techniques for privacy preserving publication of trajectories", IEEE Transactions on Knowledge and Data Engineer, Vol.29, No.7, pp.1466-1479, 2017.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (147) PDF downloads(203) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return