Privacy-Preserving Metro Passenger Flow Query System Based On Crowd-Sensing

With the continuous improvement of mobile Internet technology and the continuous improvement of various sensor functions in mobile devices,the crowd-sensing technology using mobile devices as sensing nodes has gradually become a popular and economical way of data collection.To date,numerous crowd-sensing applications,such as traffic monitoring and environmental detection,have brought great convenience to people's lives.However,these applications have the following problems: during the sensing phase,the system needs the participants to consciously or unconsciously upload the data related to their identity or location,which severely infringes on the user's location or identity privacy;In the service phase,the system requires the user to provide positioning information or query content to obtain the appropriate service,which also violates the user's location or query privacy.Once these private information is leaked,it may cause harm to user's related interests and personal safety.For example,malicious attackers may use the above information to track the user's current location or to spy on user's lifestyle through query content.For the above problems,some applications do not take corresponding measures to protect the user's privacy,but some applications propose privacy protection solutions to deal with the leakage of user privacy.However,existing privacy protection schemes do not adequately protect user's location,identity and query privacy during the sensing phase and the service phase.Therefore,in order to ensure the security of user's privacy and to ensure that users can actively participate in the crowd-sensing applications,it is necessary to take perfect privacy protection measures.The metro passenger flow query system proposed in this paper can not only provides the metro passenger flow query service,but also solves the privacy leakage problem that previous work can't take into account simultaneously.The system has the function of location-based aggregate statistics and passenger flow query,and it protects the participant's identity,location privacy and querier's query privacy.Our system mainly combines Paillier cryptosystem,pseudonym mechanism,and Secure kNN to realize the privacy preserving.Besides,our system also achieves the participant accountability.We conduct detailed experiments on the sensing phase and the query phase of the system,some of which are carried out in the Guangzhou Metro.Our experimental results demonstrate that our system has practicality: the system client only takes 129 milliseconds to take part in the sensing tasks,and the response time to the passenger flow query request of the system is within 80 milliseconds.The following security analysis also demonstrates that our system is secure.