Privacy Preserving in the Publication of Large-Scale Trajectory Databases
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 t
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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. Keywords: Privacy preserving publishing Segment clustering
Large-scale databases
Trajectory data
1 Introduction Privacy preserving issues for trajectory data publishing have a critical significance in its development, and have become a hot research topic [1]. Due to the characteristics of trajectory data such as large-scale, high-dimension, and rich-background, the research on privacy preserving issues is facing severe challenges [2, 3]. The existing privacy preserving methods for trajectory data publishing mainly transform the trajectory anonymous problem into the trajectory clustering constraint problem to protect user’s privacy [4, 5]. In those methods, the trajectory data is clustered according to the similarity firstly. Then, the generated clustering groups are transformed into corresponding anonymous groups by using the constraint operations. Among them, the constraint operations include suppression, generalization, feature release technology and space translation technology. In traditional clustering constraints based trajectory privacy preserving method, the trajectory database exist the problem of lack of diversity in the anonymous group and is vulnerable to re-clustering attacks. Furthermore, the existing trajectory anonymous techniques are mainly applied to simple trajectory database. When they are used to the anonymous issues of © Springer International Publishing Switzerland 2016 Y. Wang et al. (Eds.): BigCom 2016, LNCS 9784, pp. 367–376, 2016. DOI: 10.1007/978-3-319-42553-5_31
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large-scale trajectory databases directly, there are some problems such as more difficulty and lower data quality. Aiming at the above problems, this paper mainly focuses on the privacy preserving issues in the publishing of large-scale trajectory databases, and using the trajectory blocking techniques to improve the anonymous time and enhance the anonymous quality. A trajectory privacy preserving algorithm based on segment clustering is proposed and implemented. In our algorithm, the raw database is partiti
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