A Differential Privacy-Based Privacy-Preserving Data Publishing Algorithm for Transit Smart Card Data
Abstract
This manuscript is focused on transit smart card data and finds that the release of such trajectory information after simple anonymization creates high concern about breaching privacy. Trajectory data is large-scale, high-dimensional, and sparse in nature and, thus, requires an efficient privacy-preserving data publishing (PPDP) algorithm with high data utility. This paper describes the investigation of the publication of non-interactive sanitized trajectory data under a Differential Privacy (DP) definition. To this end, a new prefix tree structure, an incremental privacy budget allocation model, and a spatial-temporal dimensionality reduction model are proposed to enhance the sanitized data utility as well as to improve runtime efficiency. The developed algorithm is implemented and applied to real-life metro smart card data from Shenzhen, China, which includes a total of 2.8 million individual travelers and over 220 million records. Numerical analysis finds that, compared with previous work, the proposed model outputs sanitized dataset with higher utilities, and the algorithm is more efficient and scalable.
Recommended Citation
Y. Li et al., "A Differential Privacy-Based Privacy-Preserving Data Publishing Algorithm for Transit Smart Card Data," Transportation Research Part C: Emerging Technologies, vol. 115, Elsevier Ltd, Jun 2020.
The definitive version is available at https://doi.org/10.1016/j.trc.2020.102634
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Differential Privacy (DP); Privacy-Preserving Data Publishing (PPDP); Trajectory Data; Transit Smart Card
International Standard Serial Number (ISSN)
0968-090X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier Ltd, All rights reserved.
Publication Date
01 Jun 2020
Comments
Research is supported by the National Natural Science Foundation of China (Grant No. 61876043 , 61472089); NSFC -Guangdong Joint Found (Grant No. U1501254); Guangdong Provincial Key Laboratory of Cyber-Physical System (2016B030301008).