Privacy-preserving trajectory classification of driving trip data based on pattern discovery techniques
Gene P. K. Wu, Keith C. C. Chan · 2017
With the rapid growth of the remote sensing technology and its high adoption in automotive domain, identifying patterns in the context of driving trips becomes a promising and interesting area of research and application. Due to privacy concern, user location data in the moving object trajectory are to be anonymized before publishing. To classify the privacy-preserving driving trips in a set of recorded GPS tracks, this paper presents an information theoretic approach to characterize them based on their occurrences of frequently detected patterns. The patterns are discovered through a statistical significance test on a generated set of spatio-temporal data and its associated attributes that represent the characteristics of recorded GPS data. For evaluating the performance of the proposed approach, a real dataset with class information is tested to validate its classificatory power and compare with other approaches. The result indicates the approach is effective and efficient in achieving a good accuracy in the prediction of the class labels of the different driving trips based on the transformed set of attributes.