PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems
AbstractAir pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles’ location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency. View Full-Text
Share & Cite This Article
Xu, C.; Lu, R.; Wang, H.; Zhu, L.; Huang, C. PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems. Sensors 2017, 17, 500.
Xu C, Lu R, Wang H, Zhu L, Huang C. PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems. Sensors. 2017; 17(3):500.Chicago/Turabian Style
Xu, Chang; Lu, Rongxing; Wang, Huaxiong; Zhu, Liehuang; Huang, Cheng. 2017. "PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems." Sensors 17, no. 3: 500.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.