01697862

Component

With the advances in detector and sensor technology, identity detection-based intelligent transportation systems--such as license plate recognition (LPR)--have become widely applied in urban transportation, generating large quantities of individual-based mobility trace data set. Given the high frequency, precision and coverage, these mobility trace data has provided new opportunities for transportation research from transportation planning, to traffic control and management, to individual mobility pattern profiling. As a result, there is an increasing demand for publishing and sharing these individual-based data sets to researchers and practitioners. However, considering the fact that true identities of individuals can be revealed, the privacy issues have been a major concern in using these data. In this paper, the authors quantitatively measure the high risk of privacy disclosure caused by re-identification attacks based on the concept of k-anonymity. Based on a one-month LPR data collected in Guangzhou, China, the authors examine a variety of factors that may impact an individual's degree of anonymity, including the temporal granularity, the size of the published data, etc. The authors' results show that five spatiotemporal records are enough to uniquely identify about 90% of individuals even the temporal granularity is set to half a day. In order to reduce disclosure risk, they also present some ideas for protecting privacy and improving anonymization before publishing/sharing the data set. This study serves as a wake-up call for relevant agencies and data owners about the privacy vulnerability in such individual-based mobility trace data.

This paper was sponsored by TRB committee ABJ55T Task Force on Data Privacy, Security and Protection Policy.

19-01183

English

17p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Automobile drivers; Case studies; Data sharing; Detection and identification technologies; License plates; Privacy

Anonymization; License plate recognition

Guangzhou (China)

Data and Information Technology; Highways; Planning and Forecasting; Society; Vehicles and Equipment

Transportation Research Board Annual Meeting 2019 Paper #19-01183

TRIS, TRB, ATRI

Dec 7 2018 9:40AM