期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 9, 页码 16534-16547出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3130978
关键词
Trajectory; Privacy; Differential privacy; Markov processes; Water resources; Vehicular ad hoc networks; Internet of Things; Trajectory releasing; differential privacy; prefix tree; Markov based prediction
资金
- National Natural Science Foundation of China [61902110]
- National Key Research and Development Program of China [2018YFC0407105]
- Technology Project of China Huaneng Group [MW 2017/P28]
A novel differential privacy-based algorithm named DPTD is proposed for trajectory database releasing, which divides trajectory space into planes and adds noise based on spatio-temporal correlation to protect privacy while ensuring data availability. The method improves privacy protection and data availability simultaneously.
The locations and users' information can be shared and interacted in the IoV (Internet of Vehicles), which provides sufficient data for traffic deployment and behavior pattern analysis. However, privacy issues had become more severe since personal or sensitive information is inclined to be revealed in a big data environment. In this work, a novel differential privacy-based algorithm named DPTD (Differentially Private Trajectory Database) is proposed for trajectory database releasing. Firstly, a 3-dimensional generalized trajectory dataset is established by considering the time factor. Then, the trajectory space is divided into several planes through the timestamps, and the set of the locations on each plane is further processed by clustering and generalizing to re-form new trajectories, that is, the trajectories to be released. This method is quite favorable to prefix-tree releasing because the spatiotemporal characteristics of the trajectories can be captured and spareness problem is fixed. Besides, a Markov assumption-based prediction method is suggested in order to reduce the cost of adding noise. Unlike the traditional method that the noise is added layer by layer, the noise is only added to the odd layers based on the prediction through spatio-temporal correlation, saving approximately 50% of the privacy budget. Theoretical analysis and experimental results show that the proposed algorithm has better data availability than the compared algorithms while guaranteeing the expected privacy level.
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