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A Method for Time-Series Location Data Publication Based on Differential Privacy

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摘要 In the age of information sharing, logistics information sharing also faces the risk of privacy leakage. In regard to the privacy leakage of time-series location information in the field of logistics, this paper proposes a method based on differential privacy for time-series location data publication. Firstly, it constructs public region of interest (PRO!) related to time by using clustering optimal algorithm. And it adopts the method of the centroid point to ensure the public interest point (PIP) representing the location of the public interest zone. Secondly, according to the PIP, we can construct location search tree (LST) that is a commonly used index structure of spatial data, in order to ensure the inherent relation among location data. Thirdly, we add Laplace noise to the node of LST, which means fewer times to add Laplace noise on the original data set and ensures the data availability. Finally, experiments show that this method not only ensures the security of sequential location data publishing, but also has better data availability than the general differential privacy method, which achieves a good balance between the security and availability of data.
出处 《武汉大学学报:自然科学英文版》 CAS CSCD 2019年第2期107-115,共9页 Wuhan University Journal of Natural Sciences
基金 Social Science Foundation of Beijing (15JGB099. 15ZHA004) the National Natural Science Foundation of China (61370139) "Information +" Special Fund( 5111823610).
作者简介 KANG Haiyan, male. Professor. Ph.D.. research direction: information system security, and privacy preserving. E-mail: kanghaiyan@ 126.com.
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