A privacy-preserving collaborative reputation system for mobile crowdsensing. (September 2018)

• Record Type: Journal Article
• Authors: Alamri, Bayan Hashr; Monowar, Muhammad Mostafa; Alshehri, Suhair
• Is Part Of: International journal of distributed sensor networks. Volume 14:Number 9(2018)
• Publication Date: 2018-09
• Subjects: Mobile crowdsensing -- privacy -- trustworthiness -- reputation scheme; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147718802189
• Languages: English
• ISSNs: 1550-1329

Abstract

Mobile crowdsensing is an emerging technology in which participants contribute sensor readings for different sensing applications. This technology enables a broad range of sensing applications by utilizing smartphones and tablets worldwide to improve people's quality of life. Protecting participants' privacy and ensuring the trustworthiness of the sensor readings are conflicting objectives and key challenges in this field. Privacy issues arise from the disclosure of the participant-related context information, such as participants' location. Trustworthiness issues arise from the open nature of sensing system because anyone can contribute data. This article proposes a privacy-preserving collaborative reputation system that preserves privacy and ensures data trustworthiness of the sensor readings for mobile crowdsensing applications. The proposed work also counters a number of possible attacks that might occur in mobile crowdsensing applications. We provide a detailed security analysis to prove the effectiveness of privacy-preserving collaborative reputation system against a number of attacks. We conduct an extensive simulation to investigate the performance of our schema. The obtained results show that the proposed schema is practical; it succeeds in identifying malicious users in most scenarios. In addition, it tolerates a large number of colluding adversaries even if their number surpass 65%. Moreover, it detects on-off attackers even if they report trusted data with high probability (0.8).