Differentially Private Real-Time Data Publishing over Infinite Trajectory Streams

Abstract

Recent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Publishing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as “How many people are in Kyoto Station now?” However, analyzing these raw data will entail risks of compromising individual privacy. ε-Differential Privacy has emerged as a well-known standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. However, since user trajectories will be generated infinitely, it is difficult to protect every trajectory under ε-differential privacy. On the other hand, in real life, not all users require the same level of privacy. To this end, we propose a flexible privacy model of l-trajectory privacy to ensure every desired length of trajectory under protection of ε-differential privacy. We also design an algorithmic framework to publish l-trajectory private data in real time. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.