Privacy-Preserving Traffic Flow Release with Consistency Constraints

Urban traffic flow data is useful in transport ap-plications, playing an important role in various tasks such as road planning, site selection, ad services, etc. However, traffic flow data is the composition of personal driving trajectories, which can reveal sensitive information such as home and work locations, leading to privacy issues. Thus publishing traffic flow data while not disclosing private information remains a challenge for urban managers. To address this challenge, we study the noisy publication of traffic flow data in this paper. The noise is added to the data with respect to the differential privacy paradigm, which ensures data safety but deteriorates its utility. On the other hand, we find that the inherent relations of the flow data inherited from the road network structure can be used to correct data without hurting the privacy property. Hence, we propose post-processing techniques, which exploit the data's inherent relations for corrections over the global and local differentially private traffic flow data, respectively. Extensive experiments on real data show that the proposed post-processing techniques improve the data utility by 29.7%-41.1% and 17.3%-48.6% subjecting to the global and local differential privacy paradigm, respectively.