CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion

TL;DR

This paper introduces CollabLoc, a privacy-preserving multi-modal localization system that enables cross-building indoor and outdoor location tracking using existing infrastructure and collaborative data fusion, ensuring privacy and reliability.

Contribution

It presents a novel cross-building localization platform combining multiple sensors with privacy-preserving techniques and collaborative data fusion, without requiring additional infrastructure.

Findings

Achieves room-level localization accuracy

Ensures privacy through onion routing and data perturbation

Effective in indoor and outdoor environments

Abstract

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.