Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90174
Title: PAVS: a new privacy-preserving data aggregation scheme for vehicle sensing systems
Authors: Xu, Chang
Lu, Rongxing
Wang, Huaxiong
Zhu, Liehuang
Huang, Cheng
Keywords: Vehicle Sensing
Data Aggregation
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Xu, C., Lu, R., Wang, H., Zhu, L., & Huang, C. (2017). PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems. Sensors, 17(3), 500-. doi:10.3390/s17030500
Series/Report no.: Sensors
Abstract: Air 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.
URI: https://hdl.handle.net/10356/90174
http://hdl.handle.net/10220/47224
ISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s17030500
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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