A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection

Abstract

Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing. However, this way lead to a greater risk of leaking users’ privacy. To avoid the risk, this article proposes a traffic density estimation model based on crowdsourcing privacy protection. First, in the acquisition process of the track data by crowdsourcing, dual servers are employed for transmission, and homomorphic encryption is carried out to encrypt the data to protect the data from being leaked during transmission. Second, sampling is implemented for randomization and anonymization to reduce the spatial continuity and temporal continuity of position data. In this way, the intermediate server cannot acquire users’ original data, and the main server cannot obtain users’ personal information. Finally, before data transmission, Laplace noising is performed on the users’ local position data to further protect the original location information. The proposed algorithm in this study realizes that only users have their original track data, and the servers involved in the work cannot infer the original track data, which ensures the real security of user privacy. The proposed algorithm was verified with the track data from the Didi Gaia Data Opening Plan. The experimental results showed that the proposed algorithm could still maintain the validity of data analysis results and the security of user data privacy after homomorphic encryption, noise addition, and sample collection, and displayed good robustness and scalability.