A Technique of Protecting User Sensitive Partial Trajectory with Local Differential Privacy on the Road Network

Jaewon Kim, Seog Park

http://doi.org/10.5626/JOK.2020.47.7.693

Today, with the proliferation of smartphones and the development of sensor technology, path data, a list of user location data collected from mobile devices, is being manipulated for marketing or efficient algorithm development. However, such indiscriminate collection of location information may cause personal privacy leakage issues. To resolve the problem, many differential privacy techniques have been proposed. However, the previous methods significantly degrade query accuracy if they are applied in the trajectory dataset. Additionally, the differential privacy technique is classified into a curator model and a local model. The local model has advantages of not having a reliable server, but suffers from more noise inserted to reduce query accuracy. This paper classifies vertices into heavy points and light points to resolve the problem of data usability in applying differential privacy to collect road network trajectory data in the local model. Additionally, experiments show that the proposed technique mitigates the degradation of overall data usability while protecting the sensitive data in accordance with the differential privacy standards.