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Algorithms 2018, 11(12), 207;

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

Department of Computer Engineering and Informatis, University of Patras, 265 04 Patra, Greece
Department of Informatics, Ionian University, 491 00 Kerkira, Greece
Author to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 9 December 2018 / Accepted: 10 December 2018 / Published: 14 December 2018
(This article belongs to the Special Issue Humanistic Data Mining: Tools and Applications)
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In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data. The k-anonymity set consists of the k nearest neighbors. We constructed a motion vector of the form (x,y,g,v) where x and y are the spatial coordinates, g is the angle direction, and v is the velocity of mobile users, and studied the problem in four-dimensional space. We followed two approaches. The former applied only k-Nearest Neighbor (k-NN) algorithm on the whole dataset, while the latter combined trajectory clustering, based on K-means, with k-NN. Actually, it applied k-NN inside a cluster of mobile users with similar motion pattern (g,v). We defined a metric, called vulnerability, that measures the rate at which k-NNs are varying. This metric varies from 1 k (high robustness) to 1 (low robustness) and represents the probability the real identity of a mobile user being discovered from a potential attacker. The aim of this work was to prove that, with high probability, the above rate tends to a number very close to 1 k in clustering method, which means that the k-anonymity is highly preserved. Through experiments on real spatial datasets, we evaluated the anonymity robustness, the so-called vulnerability, of the proposed method. View Full-Text
Keywords: k-NN; K-means clustering; anonymity; uncertainty; trajectories k-NN; K-means clustering; anonymity; uncertainty; trajectories

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Dritsas, E.; Trigka, M.; Gerolymatos, P.; Sioutas, S. Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases. Algorithms 2018, 11, 207.

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