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
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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
(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
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.
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