Privacy-preserving high-quality map generation with participatory sensing

Abstract

Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols have been proposed that rely on volunteers to provide their traces. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. In this approach, individual users merely need to upload unorganized sparse location points so as to reduce the risk of exposing privacy, while the server generates accurate maps with unorganized points, instead of user traces. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is <; 10m, even when the collected locations are about 32m apart after clustering for the purpose of removing noise.