ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (11): 2337-2347.doi: 10.7544/issn1000-1239.2020.20190579

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Privacy Preservation Method of Data Aggregation in Mobile Crowd Sensing

Wang Taochun, Jin Xin, Lü Chengmei, Chen Fulong, Zhao Chuanxin   

  1. (School of Computer and Information, Anhui Normal University, Wuhu, Anhui 241002) (Anhui Provincial Key Laboratory of Network and Information Security(Anhui Normal University), Wuhu, Anhui 241002)
  • Online:2020-11-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61402014, 61972439, 61972438, 61871412), the CERNET Next Generation Internet Creative Project of China (NGII20170312), the Key Program of Universities Natural Science Research of the Anhui Provincial Department of Education (KJ2019A1164), the Anhui Normal University PhD Startup Fund (2018XJJ66), and the Anhui Normal University Innovation Fund (2018XJJ114).

Abstract: Serious privacy leakage problems are on the rise with the wide application of mobile crowd sensing owing to the popularity of mobile smart devices. In general, the existing privacy protection schemes assume that the third-party service platform is credible, which therefore sets a high requirement on the application context. Based on this, the paper proposes a new privacy preservation data aggregation algorithm based on elliptic curve cryptography (ECPPDA) in mobile crowd sensing. The server randomly divides the participants into g clusters and forms respective cluster public key for each cluster. The nodes in the cluster encrypt the data through their own cluster public keys and merge the data aggregation results. The server obtains the aggregation result by cooperating with the members in the cluster. Since what the server receives is the ciphertext of aggregation and the ciphertext decryption requires all the nodes in the cluster to cooperate together, the server cannot obtain the data of a single participant. In addition, the updating of the cluster public key by the server can facilitate the participants to dynamically join or leave. The experimental result shows that ECPPDA has the characteristics of high security, low consumption, low communication and high precision.

Key words: mobile crowd sensing, aggregation data, privacy preservation, collusion attack, cluster

CLC Number: