Journal on Communications ›› 2022, Vol. 43 ›› Issue (5): 1-13.doi: 10.11959/j.issn.1000-436x.2022082

• Papers •     Next Articles

Privacy-protected crowd-sensed data trading algorithm

Yong ZHANG, Dandan LI, Lu HAN, Xiaohong HUANG   

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Revised:2022-03-02 Online:2022-05-25 Published:2022-05-01
  • Supported by:
    The National Key Research and Development Program of China(2020YFE0200500);The BUPT Excellent Ph.D.Students Foundation(CX2019212)

Abstract:

To solve the problem that data privacy leakage of participants under the crowd-sensed data trading model, a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly, to achieve the privacy protection of participants, an aggregation scheme based on differential privacy was designed.Participants were no longer needed to upload raw data, but analyzed and calculated the collected data according to the task requirements, and then sent the analysis results to the platform after adding noise in accordance with the privacy budget allocated by the platform to protect their privacy.Secondly, in order to ensure the credibility of participants, a reputation model of participants was proposed.Finally, in order to encourage consumers and participants to participate in transactions, a data trading optimization model was constructed by considering the consumer’s constraint on the result deviation,the participant’s privacy leakage compensation and platform profit, and a POA based on genetic algorithm was proposed to solve the model.The simulation results show that the POA not only protects the privacy of participants, but also increases the profit of the platform by 29.27% and 20.45% compared to VENUS and DPDT, respectively.

Key words: crowd sensing, data trading, differential privacy, reputation model

CLC Number: 

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