NDLI: Differentially Private Real-Time Data Release over Infinite Trajectory Streams

Content Provider	IEEE Xplore Digital Library
Author	Yang Cao Yoshikawa, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Social Inf., Kyoto Univ., Kyoto, Japan (Yang Cao; Yoshikawa, M.)
Abstract	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.
Starting Page	68
Ending Page	73
File Size	474047
Page Count	6
File Format	PDF
e-ISBN	9781479999729
DOI	10.1109/MDM.2015.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Privacy Trajectory Data privacy Noise measurement Heuristic algorithms Approximation methods Approximation algorithms spatiotemporal data differential privacy privacy preserving data publishing
Content Type	Text
Resource Type	Article

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