دانلود رایگان مقاله حفظ حریم خصوصی هماهنگی شرکت کننده کیفیت اطلاعات برای جمع سپاری همراه

Abstract

Mobile crowdsourcing systems are important sources of information for the Internet of Things (IoT) such as gathering location related sensing data for various applications by employing ordinary citizens to participate in data collection. In order to improve the Quality of Information (QoI) of the collected data, the system server needs to coordinate participants with different data collection capabilities and various incentive requirements. However, existing participant coordination methods require the participants to reveal their trajectories to the system server which causes privacy leakage. But, with the improvement of ordinary citizens’ consciousness to protect their rights, the risk of privacy leakage may reduce their enthusiasm for data collection. In this paper, we propose a participant coordination framework, which allows the system server to provide optimal QoI for sensing tasks without knowing the trajectories of participants. The participants work cooperatively to coordinate their sensing tasks instead of relying on the traditional centralized server. A cooperative data aggregation, an incentive distribution method, and a punishment mechanism are further proposed to both protect participant privacy and ensure the QoI of the collected data. Simulation results show that our proposed method can efficiently select appropriate participants to achieve better QoI than other methods, and can protect each participant’s privacy effectively.

6. Conclusion and future work

In this paper, a privacy-preserving participant coordi- nation mechanism is proposed to both achieve optimal QoI for sensing tasks, and protect the location privacy of participants. Specifically, the cooperation among partici- pants are used to replace the traditional centralized partic- ipant coordination phase. An optimization problem is for- mulated to select participants iteratively to maximize the QoI satisfaction ratio and privacy level while fulfilling the constraints of incentive. Based on this, an approximate so- lution is proposed based on Borda Ranking method. A co- operative data aggregation method is further proposed to protect participant privacy through the whole data collec- tion procedure. And a punishment mechanism is proposed to ensure continuous operation of the whole system. Ex- tensive simulation results, based on a real trace dataset of taxi in Rome, showed the effectiveness and robustness of our approach. In the future, we plan to further consider the reputa- tion of participants for our punishment mechanism to en- sure the data quality. Extensively, we will consider not only the privacy protection issue but also the security issues in such systems. For example, we would like to investi- gate how to protect the privacy of participants when the registration server pretends itself as a smart device to spy on the trajectory information of other participants. Mean- while, we plan to explore how to further strengthen the privacy protection strategy to prevent privacy leaks in the process of information exchange between participants and server cloud.