A Correlation-Based Local Differential Privacy Scheme Design for Trajectory Data Collection

Abstract

In recent years, new industrial forms has continuously been generated with the rapid development of big data technology. However, the problem of data privacy and security has become increasingly prominent. In numerous geographically related applications, location trajectory data which involve sen- sitive personal privacy need more stringent protection measures. Thus a method has been proposed before that combining hidden Markov model with local differential privacy mechanism, for training and generating a biased trajectory dataset. Nevertheless, this method did not take into account the spatial correlation between track coordinates, which causes a large deviation be- tween the disturbed trajectory and the real trajectory. Aiming at this problem, this paper brings forward a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\varepsilon,\delta)$</tex> -local differential privacy mechanism based on trajectory coordinate correlation. On the basis of existing schemes, it emphasizes the spatial correlation of trajectory coordinates. Furthermore, it may ensure the availability of track data by reasonably setting the dynamic parameter of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta$</tex> . Finally, we conduct experiments on data sets of Taxi trajectory prediction. The results show that our Correlation Multivariate Random Response (CMRR) algorithm presents significantly better performance than Generalized Randomized Response (GRR) in data availability. In the meanwhile, it strictly meets the privacy protection requirements.