Anonymizing trajectory data: limitations and opportunities

Abstract

A variety of conditions and limiting properties complicate the anonymization of trajectory data, since they are sequential, high-dimensional, bound to geophysical restrictions and easily mapped to semantic points of interest and regions with known properties like suburban neighborhoods, industrial areas or city-centers. Learning the places where one has been is extremely privacy-invasive. However, analyzing real trajectories holds numerous promises, ranging from better informed traffic management, to location recommendations or computational social science, infrastructure and even urban development planning. The aim of this paper is to establish various challenges, stemming from ideas and also limitations of existing proposals for the anonymization of trajectories, and subsequently identify research opportunities. Keeping both utility and privacy challenges prominent, we sketch the way towards establishing a useful research framework and propose possible research venues towards privacy-preserving trajectory publication.