Research On Location Privacy Protection Mechanism In Mobile Crowdsourcing

With the implementation of wireless network technology and the popularization of smart devices,mobile crowdsourcing has developed rapidly in recent years.Mobile crowdsourcing requires workers to move to a specific area to complete the corresponding sensed tasks.In the process of sensing and data transmission,due to the continuous increase in the amount of data processing and the time consumed by the transmission,traditional cloud computing can no longer meet the demand.The emergence of distributed technologies such as edge computing and blockchain technology has effectively alleviated this problem.However,the credibility of the third-party platform cannot be determined during the interaction.At the same time,malicious attackers try to obtain workers’ information.Workers face the risk of leaking many private information.Therefore,in the process of mobile crowdsourcing,it is important to protect the privacy of workers.At this stage,privacy protection measures are mainly implemented for three types of information,namely identity information,location information and data information.This paper focuses on location privacy protection.Sensed data often has errors with real data after privacy protection processing.How to balance privacy protection and data quality has become a research hotspot in MCS.The existing privacy protection methods on MCS mostly focus on the research of privacy protection algorithms,and ignore the credibility of the interactive third-party platform.In response to the above problems,this paper divides third-party platforms into untrusted third-party platforms and trusted third-party platforms,and designs different privacy protection mechanisms,respectively.The main research contents of this paper are as follows:1.In order to solve the privacy leakage problem of untrusted third-party platforms,a privacy protection incentive mechanism for the central anonymous server is proposed in this paper.This mechanism first uses the localized differential privacy mechanism to process location information when a worker requests an anonymous service,and then uploads the processed data to the anonymous server.In response to the problem of low-quality data,a multi-strategy upload incentive mechanism based on location information is proposed,and a reward matrix is set up based on the authenticity of the data uploaded by workers to encourage workers to upload high-quality data.2.In order to solve the problem of privacy leakage of trusted third-party platforms,a distributed two-stage privacy protection mechanism based on blockchain is proposed in this paper.This mechanism uses blockchain technology to replace traditional third-party platforms to avoid the problem of third-party platforms leaking the privacy of workers’ locations.Regarding the transparency mechanism of the blockchain and malicious attacker factor,a disturbance factor is added to the localized differential privacy mechanism to further strengthen the effect of privacy protection and ensure that the data uploaded by workers to the blockchain is available but not visible.