Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

Abstract

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for special interest customers. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation from geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy reencryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency on computation and communication.

On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

“…Realizing the inevitability of personal privacy preservation in the implementation of practical MCSs, a large amount of effort has been devoted to protect the accountable information of participants in terms of true identity [13], [14], precise geological location [15], [16], [17], and plain-text sensing content [18], [19], [20]. For agent identity protection in MCSs, Ni etc.…”

“…For agent identity protection in MCSs, Ni etc. establish a strong privacy-preserving mobile crowdsensing scheme SPOON supporting accurate task allocation in [13], where all participants are well anonymised. In [14], Xiao etc.…”

“…WiFi and cellular). To preserve personal privacy, we assume that the true identities and precise geological locations of all agents are well protected by using the anonymous communication [13], [14] and spatial cloaking [15], [16] techniques, respectively. Each agent in A possesses a unique and dynamic pseudo-identity i ∈ N = {1, 2, ..., N } (where N = |A|) to communicate with s without revealing the true identity.…”

“…mobile device owners who participate in sensing tasks) is properly protected [11]. In fact, considerable efforts are being made to preserve participant's privacy in terms of their identity [13], [14], precise location [15], [16], [17], and plain-text sensor content [18], [19], [20], etc. However, with the adoption of such privacy-preserving mechanisms individual behaviors of the so called self-interested participants (or indeed their strategies) are inevitably 'blurred' from the CSP's point of view, and this potentially challenges the accuracy of the crowdsensing service.…”

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

“…The presence of malicious trustees in the system is notified to the trustor in the MCS system. To enhance system trustworthiness, it is critical for the trustor to recruit users based on their personal features, e.g., mobility pattern and reputation, although it leads to the privacy leakage of participants [45]. The sensing data collected from the surrounding areas are necessarily people-centric and related to some aspects of mobile users and their social settings: where they are and where they are going; what places they are frequently visited and what they are seeing; how their health status is and which activity they prefer to do.…”