| Title |
Persistent Transportation Traffic Volume Estimation With Differential Privacy |
| ID_Doc |
41881 |
| Authors |
Yang W.; Sun Y.-E.; Huang H.; Du Y.; Huang D.; Li F.; Luo Y. |
| Year |
2019 |
| Published |
Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 |
| DOI |
http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00136 |
| Abstract |
Traffic volume estimation is critical to the transportation engineering. Persistent traffic volume reveals the amount of core, stable traffic at locations of interest, which is meaningful to many transportation applications, such as traffic flow guidance system. Unfortunately, most of the existing state-of-the-art studies that concentrate on the persistent traffic estimation issue only provide limited privacy preservation. To tackle this challenge, we present two estimators with differential privacy respectively for estimating the persistent point traffic volume and the persistent common traffic volume in this work. We first encode the passing vehicles in privacy-preserving data structures by using the random communications between vehicles and Road-Side Units (RSUs). Then, we derive the persistent traffic estimators through mathematical analysis and bitwise operations. We also prove that the proposed schemes can achieve the ϵ-differential privacy for protecting the location and trajectory privacy of vehicles through rigorous theoretical analysis. The experimental results based on the real transportation traffic traces data demonstrate the effectiveness of the proposed estimators. © 2019 IEEE. |
| Author Keywords |
Differential privacy; Persistent traffic; Privacy preserving; Traffic volume estimation |
