Spatio-temporal Trajectory Dataset Privacy Based on Network Traffic Control
This work addresses privacy concerns for users in mobile social networks, but it appears incremental as it builds on existing differential privacy and trajectory data techniques.
The paper tackled the problem of protecting personal trajectory data in mobile social networks by using differential privacy within a network traffic control system, resulting in the proposed APTB method which addresses high dimensionality and sparsity to improve data availability.
Collection of user's location and trajectory information that contains rich personal privacy in mobile social networks has become easier for attackers. Network traffic control is an important network system which can solve some security and privacy problems. In this paper, we consider a network traffic control system as a trusted third party and use differential privacy for protecting more personal trajectory data. We studied the influence of the high dimensionality and sparsity of trajectory data sets based on the availability of the published results. Based on similarity point aggregation reconstruction ideas and a prefix tree model, we proposed a hybrid publishing method of differential privacy spatiotemporal trajectory data sets APTB.