Artificial Impostors for Location Privacy Preservation

The progress of location-based services has led to serious concerns on location privacy leakage. For effective and efficient location privacy preservation (LPP), existing methods are still not fully competent. They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a \emph{scalable} LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them through synthesizing \emph{artificial impostors} (AIs). The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do \emph{not} contain any target location. Two dedicated techniques are devised: the \emph{sampling-based synthesis method} and \emph{population-level semantic model}. They play significant roles in two critical steps of synthesizing AIs. We conduct experiments on real datasets in two cities (Shanghai, China and Asturias, Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of $97.65\%$ and $96.12\%$, and its run time of building the generators is only $230.47$ and $215.92$ seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations.