4.6 Article

Privacy-preserving location-based traffic density monitoring

Journal

CONNECTION SCIENCE
Volume 34, Issue 1, Pages 874-894

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/09540091.2021.1993137

Keywords

Location-based services; traffic density monitoring; k-anonymity; dummy location; privacy protection

Funding

  1. Natural Science Foundation of Shandong Province [ZR2020MF056, ZR2020KF011]
  2. Natural Science Foundation of China [62071280]
  3. Major Scientific and Technological Innovation Project of Shandong Province [2020CXGC010115]

Ask authors/readers for more resources

The study proposed a traffic density monitoring system that protects the location privacy of vehicles and the query privacy of users by adding a pseudonym server and a location anonymisation server, improving anonymity success rate and location privacy security.
Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available