Abstract
In recent years, preserving individual privacy when publishing trajectory data receives increasing attention. However, the existing trajectory data privacy preserving techniques cannot resolve the anonymous issues of large-scale trajectory databases. In traditional clustering constraint based trajectory privacy preserving algorithms, the anonymous groups lack of diversity and they cannot effectively prevent re-clustering attacks against the characteristics of publishing data. In this thesis, a segment clustering based privacy preserving algorithm is proposed. Firstly, the original database is divided into blocks and each block is treated as a separate database. Then, the trajectories in each block are partitioned into segments based on the minimum description length principle. Lastly, these segments are anonymized with cluster-constraint strategy. Experimental results show that the proposed algorithm can improve the safety and have good performance in data quality and anonymous efficiency.
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Acknowledgement
This work is supported by Fundamental Research Funds for the Central Universities (N130316001, N140404006) and the National Natural Science Foundation of China under Grant No. 61173027.
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Li, F., Gao, F., Yao, L., Pan, Y. (2016). Privacy Preserving in the Publication of Large-Scale Trajectory Databases. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_31
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DOI: https://doi.org/10.1007/978-3-319-42553-5_31
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