Computer Science ›› 2021, Vol. 48 ›› Issue (10): 266-271.doi: 10.11896/jsjkx.200900021

• Information Security • Previous Articles     Next Articles

Location Privacy Game Mechanism Based on Generative Adversarial Networks

WEI Li-qi, ZHAO Zhi-hong, BAI Guang-wei, SHEN Hang   

  1. College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
  • Received:2020-09-02 Revised:2020-11-06 Online:2021-10-15 Published:2021-10-18
  • About author:WEI Li-qi,born in 1994,postgraduate.His main research interests include location privacy protection.
    BAI Guang-wei,born in 1961,Ph.D,professor,doctoral supervisor,is a member of China Computer Federation Outstanding.His mainresearch in-terests include mobile Internet,network security,location services and so on.
  • Supported by:
    National Natural Science Foundation of China(61502230) and Natural Science Foundation of Jiangsu Province,China(BK20150960).

Abstract: This paper proposes a user-centered location privacy game mechanism,which is aimed to generate corresponding protection strategies based on the LBS service quality,and reduce the calculation scale and utility loss.This mechanism is based on the Stackelberg game model.When a user requests a LBS service,he/she uses the location ambiguity mechanism to disturb his/her location and send it to the LBS server,making it difficult for the attacker to predict his/her real location.Based on part of their known background knowledge,attackers infer the protection policies of users in the anonymous area and adjust their attack methods to minimize the level of user privacy.In order to solve the problem of large scale and long time calculation by traditional mathematical methods,this paper adopts generating countermeasures network to participate in the generation of protection strategy,and reduces the utility cost as much as possible.The experimental results show that the protection mechanism has good performance in terms of privacy protection level,and at the same time,it significantly reduces the generation time of the protection mechanism while losing some quality of service.

Key words: Game theory, Generative adversarial networks, Machine learning, Privacy protection, Service quality

CLC Number: 

  • TP393
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