DTCS: An integrated strategy for enhancing data trustworthiness in mobile crowdsourcing
IEEE Internet of Things
Institute of Electrical and Electronics Engineers (IEEE)
© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
IEEE Mobile Crowdsourcing Systems (MCS) are important sources of information for the positioning services in IoT such as gathering location information through employing citizens to participate in data collection. Although MCS have attracted significant research and development efforts, there are salient open issues and challenges in security and privacy for MCS, which is an essential factor for its success. This paper proposes an integrated strategy named DTCS to enhance data trustworthiness and defend against the internal threats for mobile crowdsourcing. The DTCS integrates effective methods including an evaluation scheme for the attribute relevancy and familiarity of participants, a trust relationship establishment method, a group division strategy based on attributes and metagraph, and a core-selecting based incentive mechanism. The simulation results show that the DTCS improves the performance of the crowdsourcing strategy compared to the state-of-the-art including the TSCM and PPPCM. The DTCS can effectively defend against internal conflicting behaviour attacks and collusion attacks to enhance data trustworthiness for mobile crowdsourcing.
This work was supported by the National Natural Science Foundation of China (61363068, 61472083, 61402110, 61472121), Pilot Project of Fujian Province (2016Y0031), the Foundation of Science and Technology on Information Assurance Laboratory (KJ-14-109), and the GDAS Special Project of Science and Technology Development (2017GDASCX-0101).
This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.
Published online 2 February 2018