  Mobile phones provide an excellent opportunity for building context-aware
applications. In particular, location-based services are important
context-aware services that are more and more used for enforcing security
policies, for supporting indoor room navigation, and for providing personalized
assistance. However, a major problem still remains unaddressed---the lack of
solutions that work across buildings while not using additional infrastructure
and also accounting for privacy and reliability needs. In this paper, a
privacy-preserving, multi-modal, cross-building, collaborative localization
platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular
RSSI, sound and light levels, that enables room-level localization as main
application (though sub room level granularity is possible). The privacy is
inherently built into the solution based on onion routing, and
perturbation/randomization techniques, and exploits the idea of weighted
collaboration to increase the reliability as well as to limit the effect of
noisy devices (due to sensor noise/privacy). The proposed solution has been
analyzed in terms of privacy, accuracy, optimum parameters, and other overheads
on location data collected at multiple indoor and outdoor locations using an
Android app.
