计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 266-271.doi: 10.11896/jsjkx.200900021

• 信息安全 • 上一篇    下一篇

基于生成对抗网络的位置隐私博弈机制

魏礼奇, 赵志宏, 白光伟, 沈航   

  1. 南京工业大学计算机科学与技术学院 南京211816
  • 收稿日期:2020-09-02 修回日期:2020-11-06 出版日期:2021-10-15 发布日期:2021-10-18
  • 通讯作者: 白光伟(bai@njtech.edu.cn)
  • 作者简介:201861220045@njtech.edu.cn
  • 基金资助:
    国家自然科学基金(61502230);江苏省自然科学基金(BK20150960)

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).

摘要: 文中提出了一种以用户为中心的位置隐私博弈机制,目的是在满足LBS服务质量的基础上生成对应的保护策略,并减小计算规模和效用损失。该机制以Stackelberg博弈模型为基础,用户在请求LBS服务时,采用位置模糊机制对自身位置进行扰动后发送给LBS服务器,使攻击者难以推测自己的真实位置;攻击者根据已知的一部分背景知识,对匿名区域内用户的保护策略进行推断并调整攻击方式,最小化用户隐私水平。为了解决传统线性规划解法在现实场景中计算复杂度过高、实用性低的问题,文中采用生成对抗网络参与保护策略的生成,并尽可能降低效用代价。实验结果表明,该保护机制在隐私保护水平方面有着良好的表现,在损失一定服务质量的同时明显缩短了保护机制的生成时间。

关键词: 博弈论, 服务质量, 机器学习, 生成对抗网络, 隐私保护

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

中图分类号: 

  • TP393
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