Differentially Private Real-Time Data Release over Infinite Trajectory Streams

Recent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Releasing 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 Central Station now?" However, analyzing these raw data will entail risks of compromising individual privacy. ϵ-Differential Privacy has emerged as a de facto standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. Since user trajectories will be generated infinitely, it is difficult to protect every trajectory under ϵ-differential privacy. To this end, we propose a flexible privacy model of ℓ-trajectory privacy to ensure every length of ℓ trajectories under protection of ϵ-differential privacy. Then we hierarchically design algorithms to satisfy ℓ-trajectory privacy. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.