Privacy preserving and data publication for vehicular trajectories with differential privacy

Abstract In the Vehicular Ad-hoc Networks, an enormous number of Location-based Services could be given to clients as per their development characteristics. Then, protection might be spilt when clients distribute certain vehicular trajectories information to the servers. Location-based Services collect large amounts of the Vehicular Ad-hoc Networks trajectories data, and if this data is released directly without any processing, it leaks the vehicles privacy. Nowadays, many scientists have encouraged different technologies to protect privacy, but how to use it rationally for Location-based Service is also a challenge. This path is continuous in time and space. Still, mostly the existing approaches only acknowledge a single position of the moving object at a particular time. They do not find the entire path, which may destroy the integrity of the space–time of the trajectory of the vehicle. However, existing work cannot fully guarantee the privacy of the vehicle’s trajectories because randomly selected noise does not contribute to the dissemination of meaningful path data, and people must hide access to sensitive areas. In this paper, a Differential Privacy and generalization based annonymization approach is proposed to protect the privacy of the sensitive vehicular trajectories. Privacy analysis shows that this scheme is achieved the Differential Privacy. The experiments with vehicular trajectories data shows that the system has good data feasibility and can be applied on large vehicular trajectories datasets. In the results firstly, to compute the Dump Ratio and CAVG experiment to check the efficiency of the method. Results shows the histogram of DPPS, PPDP and NTDP and the proposed strategy on the four datasets, where DPPS, PPDP and NTDP have lower accessibility contrasted with the proposed technique. The accuracy, precision and the recall rate of proposed method is also achieved. The impact of privacy budget values on Relative Average Error, Mean Absolute Error, Standard Deviation are also examined.