  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 scalable LPP method based on the paradigm of
counterfeiting locations. To make fake locations extremely plausible, we forge
them through synthesizing artificial impostors (AIs). The AIs refer to the
synthesized traces which have similar semantic features to the actual traces,
and do not contain any target location. Two dedicated techniques are devised:
the sampling-based synthesis method and 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.
