Privacy-Preserving Compressive Sensing for Traffic Estimation

Traffic estimation is a popular approach to acquire traffic conditions in urban areas. At present, using the traffic data to realize the low-cost traffic estimation has already been widely favored. Although those data include various sensitive element, people ignore the harm caused by information leakage while the data are used. Additionally, the transmission of vehicle data also requires a very large communication bandwidth. To address those problems, we focus on the privacy-preserving vehicle data and reducing the amount of ciphertext data to achieve a city-scale traffic estimation. Meanwhile, we present a novel framework that integrates compressive sensing (CS) technology into privacy- preserving vehicle data. Furthermore, outsourcing vehicle data to the cloud is adopted to overcome the limitations of the in-vehicle sensors. In particular, we present a feasible computational scheme for traffic estimation, further improve the capacity of privacy- preserving and decrease system energy consumption. Finally, we validate the effectiveness of the scheme proposed through the real-world dataset.