PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

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.

Efficient Privacy-Preserving Data Sharing for Fog-Assisted Vehicular Sensor Networks

“…Some works closely related to this paper are briefly reviewed below. In F-VSNs, massive sensory data is produced in each data dimension, and needs to be uploaded for further processing and analysis; data aggregation schemes [16][17][18][19][20][21][22][23] have received considerable attention recently, and are roughly classified into two categories: single-dimensional data aggregation [16][17][18][19] and multi-dimensional data aggregation [20][21][22][23]. Zhuo et al [16] introduced a data aggregation scheme, which protects each involved entity's identity privacy, and allows the requester to examine the correctness of the obtained results.…”

“…However, using the existing data aggregation schemes [16][17][18][19][20][21][22] cannot determine the number of data reports produced in each road segment, and cannot compute the average sensory data in each road segment. To solve the problem, the scheme [23] exploits the Chinese remainder theorem and Paillier cryptosystem to calculate the average sensory data in each segments; however, it brings heavy computation and communication costs.…”

Privacy-friendly spatial crowdsourcing in vehicular networks

“…As discussed in many recent research papers [4,[10][11][12] related to spatial crowdsourcing, a significant barrier of the successful crowdsourcing applications is the privacy concern. Hence, based on the system model, we first define the trust levels of all entities from a real-world perspective, and describe the appropriate privacy requirements for protecting workers' location privacy.…”

“…Generally, there are two types of spatial crowdsourcing schemes based on different data sources (i.e., workers), namely: mobile device users [23] and vehicles [24] . With the rapid development of VANET and communication technology, vehicle-based crowdsourcing applications have become increasingly realistic and many crowdsourcing applications have been proposed, for example, realtime navigation [25] , air-quality sensing [11,12] , and traffic monitoring [26] .…”

A Variable Weight Privacy-Preserving Algorithm for the Mobile Crowd Sensing Network

“…Jin proposed a differentially private incentive mechanism that preserves the privacy of each worker's bid against the other honest-but-curious workers [3]. Furthermore, many researchers focused on the detailed information extraction processing in MCS including Hybrid Deep Learning Architecture [4] and Fog Computing and Data Aggregation Scheme [5,6].…”