A Study of Enhancing Privacy for Intelligent Transportation Systems: $k$ -Correlation Privacy Model Against Moving Preference Attacks for Location Trajectory Data
Peipei Sui, Xianxian Li, Yan Zhi Bai · IEEE Access · 2017
Internet of Things (IoT) has been widely used in various application domains including smart city, environment monitoring and intelligent transportation systems. Thousands of interconnected IoT devices produce an enormous volume of data termed as big data. However, privacy protection has become one of the biggest problems with the progress of big data. Personal privacy is usually challenged by the development of technology. In this paper, we focus on privacy protection for location trajectory data, which is collected in intelligent transportation system. First, we demonstrate that the moving preference of individuals can be exploited to perform re-identification attacks, which may cause serious damage to the identity privacy of users. To address this re-identification problem, we present a new trajectory anonymity model, in which the degree of correlation between parking locations and individuals is precisely characterized by a concept of Location Frequency-inverse user frequency (LF-IUF, for short). We then propose an anonymizing method to replace parking locations by a k-correlation region. Our method provides a novel anonymity solution for publishing trajectory data, which achieves a better trade off between privacy and utility. Finally, we run a set of experiments on real-world data sets, and demonstrate the effectiveness of our method.