The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for efficient and scalable location privacy preservation (LPP). They are often vulnerable to inference attacks with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection quality with low computational complexity. We propose ascalableLPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them by synthesizingartificial impostors. The so-called artificial impostors refer to the synthesized traces that have similar semantic features to the actual traces, e.g., similar transition patterns, and do not contain anyprotected location, i.e., the exact location that needs to be protected. We devise two dedicated techniques: thestation-based synthesis methodand thepopulation-level semantic model. We conduct the experiments on real datasets of two cities (Shanghai of China and Asturias of Spain) to validate the quality of privacy preservation, utility loss, and scalability of the proposed method. Based on these two datasets, the experimental results show that our method achieves the privacy preservation quality of$97.68\%$and$96.24\%$, respectively, and the time spent on building generators is only 144.96 seconds and 136.08 seconds, respectively. The experimental results also show that our method achieves a good trade-off between privacy and utility. Our study would give the research community new insights into improving the practicality of LPP paradigm via counterfeiting locations.

The average travel speed on certain road sections is an important piece of information for the intelligent transportation system. Traditional ways for estimating travel speed usually depend on dedicated sensors or infrastructures, which is financially costly. Alternatively, as an infrastructure-free way, vehicular crowdsensing can be used to collect data including real-time locations and velocities of vehicles, which is quite low cost and effective. This paper aims to produce a fully covered distribution of average travel speeds for road sections both in time and space domains based on vehicular crowdsensing data. However, due to the uneven spatial-temporal distribution of vehicles and the variation of their data-offering intervals, vehicular crowdsensing data are usually coarse grained. This coarseness leads to missing travel speed values of vehicles on some road sections. To handle this problem, we propose an approach that exploits the spatial-temporal causality among travel speeds of road sections by a time-lagged correlation coefficient function. We use a time-lagging factor to quantify the time consumption of vehicles traveling along road sections. Then, we utilize the local stationarity of correlation coefficient to estimate the travel speeds of road sections. Experiments based on real taxi trace data show that the proposed method performs better than some methods in use.