Privacy Preservation in the Publication of Trajectories

By Manolis Terrovitis, Nikos Mamoulis. International Conference on Mobile Data Management, 2008

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Trajectory Data Mining (2015, background): “Major techniques for protecting users’ privacy in such scenario include, clustering-based [1], generalization-based [72], suppression-based [98], and gridbased approach [35].”
Computing with Spatial Trajectories (2011, background): “, [53, 154]), researchers have addressed the problem of generic representation of uncertainty, along with a framework for syntactic categorization of spatio-temporal queries [88, 103, 171].”
Semantic trajectories modeling and analysis (2013, background): “This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies.”
Differentially private sequential data publication via variable-length n-grams (2012, background): “Due to the ubiquitousness of trajectory (sequential) data, some recent works [1], [21], [24], [12], [5], [18] have been done on privacy-preserving trajectory data publishing.”