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http://dx.doi.org/10.9708/jksci.2021.26.12.019

Privacy-Preserving Traffic Volume Estimation by Leveraging Local Differential Privacy  

Oh, Yang-Taek (Dept. of Computer Science, Sangmyung University)
Kim, Jong Wook (Dept. of Computer Science, Sangmyung University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.12, 2021 , pp. 19-27 More about this Journal
Abstract
In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.
Keywords
Local difference privacy; Traffic volume estimation; Deep learning; Data Privacy;
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