PROJECT TITLE :

Protecting Multi-Lateral Localization Privacy in Pervasive Environments

ABSTRACT:

Location-primarily based services (LBSs) have raised serious privacy issues in the society, because of the possibility of leaking a mobile user's location data in enabling location-dependent services. Whereas existing location-privacy studies are mainly focused on preventing the leakage of a user's location in accessing the LBS server, the potential privacy leakage within the calculation of the user's location, i.e., the localization, has been largely ignored. Such a privacy leakage stems from the fact that a localization algorithm sometimes takes the location of anchors (reference points for localization) as input, and generates the target's location as output. As such, the situation of anchors and target could be leaked to others. An adversary might any utilize the leakage of anchor's locations to attack the localization infrastructure and undermine the correct estimation of the target's location. To address this issue, during this paper, we have a tendency to study the multi-lateral privacy-preserving localization problem, whereby the placement of a target is calculated without the necessity of showing anchors' location, and also the information of the localization outcome, i.e., the target's location, is strictly restricted to the target itself. To totally protect the user's privacy, our study protects not solely the user's precise location data (the geo-coordinates), but also any aspect data which will lead to a rough estimate of the location. We formulate the matter as a secure least-squared-error (LSE) estimation for an overdetermined linear system and develop 3 privacy-preserving solutions by leveraging combos of data-hiding and homomorphic encryption. These solutions give completely different levels of protection for location-facet info and resilience to node collusion and have the advantage of having the ability to trade a user's privacy needs for higher computation and communication efficiency. Through numerical results, we verify the significant efficiency improvemen- of the proposed schemes over existing multiparty secure LSE algorithms.