ESPPTD:An efficient slicing-based privacy-preserving truth discovery in mobile crowd sensing

2021 ◽  
pp. 107349
Author(s):  
Chengmei Lv ◽  
Taochun Wang ◽  
Chengtian Wang ◽  
Fulong Chen ◽  
Chuanxin Zhao
Keyword(s):  
Privacy Preserving ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Mobile Crowd

Efficient and Privacy-Preserving Truth Discovery in Mobile Crowd Sensing Systems

2019 ◽  
Vol 68 (4) ◽  
pp. 3854-3865 ◽  
Author(s):  
Guowen Xu ◽  
Hongwei Li ◽  
Sen Liu ◽  
Mi Wen ◽  
Rongxing Lu
Keyword(s):  
Privacy Preserving ◽  
Mobile Crowd Sensing ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Mobile Crowd

Bayesian Co-Clustering Truth Discovery for Mobile Crowd Sensing Systems

2020 ◽  
Vol 16 (2) ◽  
pp. 1045-1057 ◽  
Author(s):  
Yang Du ◽  
Yu-E Sun ◽  
He Huang ◽  
Liusheng Huang ◽  
Hongli Xu ◽  
...  
Keyword(s):  
Mobile Crowd Sensing ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Mobile Crowd

scholarly journals Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Chen ◽  
Shengbao Zheng ◽  
Zhengqiu Weng
Keyword(s):  
Mobile Node ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Node Mobility ◽  
Mobile Nodes ◽  
Mobile Crowd Sensing ◽  
Sensitive Data ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Mobile Crowd

Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach called P3 to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper-layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.

Location Privacy-Preserving Method for Auction-Based Incentive Mechanisms in Mobile Crowd Sensing

2017 ◽  
Vol 61 (6) ◽  
pp. 937-948
Author(s):  
Tong Liu ◽  
Yanmin Zhu ◽  
Ting Wen ◽  
Jiadi Yu
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowd Sensing ◽  
Incentive Mechanisms ◽  
Crowd Sensing ◽  
Mobile Crowd

scholarly journals A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Taochun Wang ◽  
Chengmei Lv ◽  
Chengtian Wang ◽  
Fulong Chen ◽  
Yonglong Luo
Keyword(s):  
Data Aggregation ◽  
Rapid Development ◽  
Mobile Crowd Sensing ◽  
Sense Data ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Head Node ◽  
True Values ◽  
Mobile Crowd

With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

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