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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Beijing Foreign Studies Univ Int Business Sch Beijing 100089 Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Dalian Univ Technol Fac Econ & Management Dalian 116000 Peoples R China
出 版 物:《IEEE ACCESS》 (IEEE Access)
年 卷 期:2019年第7卷
页 面:155851-155859页
核心收录:
基 金:National Science Foundation of China [71802037, J1824031] Fundamental Funds for the Ministry of Education in China (MOE) Project of Humanities and Social Sciences [19YJC630137, 19YJC630043] Fundamental Research Funds for the Central Universities [3132019223, 3132019224]
主 题:Task analysis Privacy Quality control Crowdsourcing Uncertainty Entropy Data privacy Spatial crowdsourcing obfuscation location privacy of workers quality control EM algorithm
摘 要:Emerging spatial crowdsourcing (SC) provides an approach for collecting and analyzing spatiotemporal information from intelligent transportation systems. However, the exposure of massive location privacy to potential adversaries for the purpose of quality control makes workers more vulnerable. To protect workers location privacy, an obfuscation scheme is proposed to incorporate uncertainties into the SC quality control problem through obfuscating the standard location data in terms of both space and time. Two measures, location entropy and results accuracy, are used to evaluate the performance of location privacy protection. We theoretically and experimentally confirm the security and accuracy of the obfuscation approach. The results of experiments show that: a) hiding workers location from the requester reduces the quality of SC;and b) obfuscation arithmetic with appropriate obfuscation coefficients protects workers location privacy with little effect on SC quality. Under the protection of this obfuscation scheme, the new system provides better security and similar quality compared to the existing SC system.