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

• Published in: Algorithms Vol. 11; no. 12; p. 207
• Main Authors: Dritsas, Elias, Trigka, Maria, Gerolymatos, Panagiotis, Sioutas , Spyros
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: Algorithms; anonymity; Classification; Clustering; Data mining; Datasets; Global positioning systems; GPS; K-means clustering; k-NN; Machine learning; Methods; Privacy; Robustness; Social networks; Trajectories; uncertainty
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a11120207

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.