FCT: a fully-distributed context-aware trust model for location based service recommendation

  • Zhiquan Liu
  • Jianfeng MaEmail author
  • Zhongyuan Jiang
  • Yinbin Miao
Research Paper


With the popularity of location based service (LBS), a vast number of trust models for LBS recommendation (LBSR) have been proposed. These trust models are centralized in essence, and the trusted third party may collude with malicious service providers or cause the single-point failure problem. This work improves the classic certified reputation (CR) model and proposes a novel fully-distributed context-aware trust (FCT) model for LBSR. Recommendation operations are conducted by service providers directly and the trusted third party is no longer required in our FCT model. Besides, our FCT model also supports the movements of service providers due to its self-certified characteristic. Moreover, for easing the collusion attack and value imbalance attack, we comprehensively consider four kinds of factor weights, namely number, time decay, preference and context weights. Finally, a fully-distributed service recommendation scenario is deployed, and comprehensive experiments and analysis are conducted. The results indicate that our FCT model significantly outperforms the CR model in terms of the robustness against the collusion attack and value imbalance attack, as well as the service recommendation performance in improving the successful trading rates of honest service providers and reducing the risks of trading with malicious service providers.


trust model fully-distributed context-aware location based service service recommendation 



This work was supported by National High Technology Research and Development Program (863 Program) (Grant No. 2015AA016007), National Natural Science Foundation of China (Grant No. 61502375, 61370078), Key Program of NSFC (Grant No. U1405255), and Natural Science Basis Research Plan in Shaanxi Province of China (Grant No. 2016JQ6046).


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Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Zhiquan Liu
    • 1
  • Jianfeng Ma
    • 1
    • 2
    Email author
  • Zhongyuan Jiang
    • 2
  • Yinbin Miao
    • 2
  1. 1.School of Computer Science and TechnologyXidian UniversityXi’anChina
  2. 2.School of Cyber EngineeringXidian UniversityXi’anChina

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