CollabLoc: Privacy-Preserving Multi-Modal Collaborative Mobile Phone Localization

Abstract

Mobile 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. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also 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 solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.