Privacy Enabled Crowdsourced Transmitter Localization Using Adjusted Measurements

We address the problem of location privacy in the context of crowdsourced localization of spectrum offenders where participating receivers report received signal strength (RSS) measurements and their location to a central controller. We present a novel approach, that we call the adjusted measurement approach, in which we generate pseudo-locations for participating receivers and report these pseudo-locations along with adjusted RSS measurements as if the measurements were made at the pseudo-locations. The RSS values are adjusted by representing those as a weighted linear combination of the RSS values at the receivers, where receivers closer to the false location have a higher weight than those far away. We use two RSS datasets, one from a cluttered office (indoor) and another from roadways in Phoenix, Arizona (outdoor) to evaluate our approach. We compare the localization error of our approach with that of the naive approach that simply adds noise to locations. Our results demonstrate that location privacy can be preserved without a significant increase in the localization error. We also formulate an adversary attack that attempts to solve the inverse problem of determining the true locations of the receivers from their false locations. Our evaluations show that the adversary does no better than random guessing of true locations in the monitored area.