Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (7): 397-404.doi: 10.11896/jsjkx.230400181

• Information Security • Previous Articles     Next Articles

Privacy Incentive Mechanism for Mobile Crowd-sensing with Comprehensive Scoring

FU Yanming, ZHANG Siyuan   

  1. School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
  • Received:2023-04-24 Revised:2023-07-20 Online:2024-07-15 Published:2024-07-10
  • About author:FU Yanming,born in 1976,Ph.D,associate professor.His main research intere-sts include data mining,network security,crowd-sensing,etc.
    ZHANG Siyuan,born in 1999,postgra-duate.His main research interests include mobile crowd-sensing and incentive mechanism,privacy protection.
  • Supported by:
    National Natural Science Foundation of China(61962005).

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Abstract: The efficient operation of mobile crowd-sensing(MCS) largely depends on whether a large number of users participate in the sensing tasks.However,in reality,due to the increase of user's sensing cost and the privacy disclosure of users,the users' participation enthusiasm is not high,so an effective mean is needed to ensure the privacy security of users,and it can also promote users to actively participate in the tasks.In response to the above issues,a new privacy incentive mechanism of bilateral auction with comprehensive scoring(BCS) based on local differential privacy protection technology is proposed.This incentive mechanism includes three parts:auction mechanism,data perturbation and aggregation mechanism,and reward and punishment mechanism.The auction mechanism comprehensively considers the impact of various factors on users' sensing tasks,to some extent,it improves the matching degree of tasks.The data perturbation and aggregation mechanism makes a balance between privacy protection and data accuracy,and achieves good protection of user privacy while ensuring data quality.The reward and punishment mechanism rewards users of high integrity and activity to encourage users to actively participate in sensing tasks.Experimental results indicate that BCS can improve platform revenue and task matching rate while ensuring the quality of sensing data.

Key words: Mobile crowd-sensing, Incentive mechanism, Privacy protection, Comprehensive scoring, Data perturbation and aggregation

CLC Number: 

  • TP391
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