A Privacy-preserving Data Aggregation of Mobile Crowdsensing Based on Local Differential Privacy
Mobile crowdsensing(MCS)is increasingly being used in smart city research to collect data,such as environmental assessment and traffic monitoring.However,this approach introduces a number of privacy and efficiency challenges,as sensing report includes the user's sensitive location and assigned attributes.Many methods adopt differential privacy scheme to protect users'privacy,while the assumption that the server is trusted is not realistic in practi-cal application.Recently,local differential privacy has paved the way for more efficient and private data collection for the untrusted model,though it remains a challenge to obtain effective statistical analysis when applied to small and medium-sized MCS tasks.In this paper,we improve the local e-differential privacy method for MCS data aggregation to preserve participant privacy and achieve accu-rate data analysis.Considering the different attributes of sensing data,we first adopt a distinct local differential privacy procedure to diverse sensing attributes.Then we propose a data aggregation algorithm to count and remove the noise data provided by partici-pants.Simulation results show that the proposed scheme improves analysis accuracy and reduces the lowest number of participants in a task,compared with existing similar solutions.
Local differential privacy mobile crowdsensing location privacy joint distribution
Fan Peng Shaohua Tang Bowen Zhao Yuxian Liu
South China University of Technology Guangzhou,China
国际会议
2019国图灵大会(ACM Turing Celebration conference-China 2019 )
成都
英文
559-563
2019-05-17（万方平台首次上网日期，不代表论文的发表时间）