3 years ago

Artificial Impostors for Location Privacy Preservation.

Cheng Wang, Zhiyang Xie

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.

Publisher URL: http://arxiv.org/abs/1801.06827

DOI: arXiv:1801.06827v1

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.