P \({ }^3\) -LOC: A Privacy-Preserving Paradigm-Driven Framework for Indoor Localization

Indoor localization plays an important role as the basis for a variety of mobile applications such as navigating, tracking, and monitoring in indoor environments. However, many such systems cause potential privacy leakage in data transmission between mobile users and the localization server (LS). Unfortunately, there has been little research done on privacy issue, and existing privacy-preserving solutions are algorithm-driven, each designed for specific localization algorithms, which hinders their wide-scale adoption. Furthermore, they mainly focus on users’ location privacy, while the LS’s data privacy cannot be guaranteed. In this chapter, we propose P \({ }^3\) -LOC, a Privacy-Preserving Paradigm-driven framework for indoor LOCalization. P \({ }^3\) -LOC takes the advantage that most indoor localization systems share a common two-stage localization paradigm: information measurement and location estimation. Based on this, P \({ }^3\) -LOC carefully perturbs and cloaks the transmitted data in these two stages and employs specially designed “ \(\boldsymbol k\) -anonymity” and “differential privacy” techniques to achieve the provable privacy preservation. The key advantage is that P \({ }^3\) -LOC does not rely on any prior knowledge of the underlying localization algorithms, and it guarantees both users’ location privacy and the LS’s data privacy. Our extensive experiments from measured data have validated that P \({ }^3\) -LOC provides privacy preservation for general indoor localization techniques. In addition, P \({ }^3\) -LOC is comparable to the state-of-the-art algorithm-driven techniques in terms of localization error, computation, and communication overhead.