Preserving Privacy in Semantic-Rich Trajectories of Human Mobility
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The increasing abundance of data about the trajectories of...
The increasing abundance of data about the trajectories of
personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge
since it opens new ways of intruding into personal privacy.
Representing the personal movements as sequences of places
visited by a person during her/his movements - semantic
trajectory - poses even greater privacy threats w.r.t. raw
geometric location data. In this paper we propose a privacy model deﬁning the attack model of semantic trajectory
linking, together with a privacy notion, called c-safety. This
method provides an upper bound to the probability of inferring that a given person, observed in a sequence of non-sensitive places, has also stopped in any sensitive location.
Coherently with the privacy model, we propose an algorithm
for transforming any dataset of semantic trajectories into a
c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting
quality/utility measures of the original trajectories, such as
sequential pattern mining results.
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