Protecting personal trajectories of social media users through differential privacy

Road traffic congestion is an important issue in modem cities, however most existing traffic jam identification solutions are based on expensive facilities such as sensors or transport probe infrastructure with high deployment and management costs. As a result of the cost, such solutions are not ubiquitously deployed. The extensive use of smart mobile devices furnished with location-based competencies and the global popularity of microblogging applications like Twitter offers an opportunity to tackle these problems. Twritter users can serve as human traffic sensors providing real-time reflections of current traffic situations. However, these data can contain extensive personal privacy information that demands privacy preserving mechanisms for the user location and their current trajectory. Differential privacy can ensure that degrees of privacy in these trajectories can be preserved whilst allowing data analysis and mining of the Twitter content. This paper proposes an innovative private trajectories release model and associated algorithms with differential privacy guarantees that considers both data privacy and data utility. This includes development of a private reference system for calibrating separate (raw) users trajectories across obfuscated anchor points; construction of privacy supporting noise-enhanced prefix trees to release synthesis data privately, and comprehensive evaluation of both the accuracy and utility of the solutions in terms of a set of evaluation metrics based on real-life tweets-based user trajectories across the city of Melbourne. (C) 2017 Published by Elsevier Ltd.