Protecting personalized privacy against sensitivity homogeneity attacks over road networks in mobile services

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• 作者：Xiao Pan ; Weizhang Chen ; Lei Wu ; Chunhui Piao ; Zhaojun Hu
• 关键词：query semantics ; sensitive information ; privacy protection ; road networks ; location based services
• 刊名：Frontiers of Computer Science in China
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：10
• 期：2
• 页码：370-386
• 全文大小：1,164 KB
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• 作者单位：Xiao Pan (1)
  Weizhang Chen (1)
  Lei Wu (1)
  Chunhui Piao (1)
  Zhaojun Hu (1)
  
  1. Schod of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention to prevent sensitive information disclosure using query semantics. In terms of personalized privacy requirements, all queries in a cloaking set, from some user’s point of view, are sensitive. These users regard the privacy is breached. This attack is called as the sensitivity homogeneity attack. We show that none of the existing location cloaking approaches can effectively resolve this problem over road networks. We propose a (K, L, P)-anonymity model and a personalized privacy protection cloaking algorithm over road networks, aiming at protecting the identity, location and sensitive information for each user. The main idea of our method is first to partition users into different groups as anonymity requirements. Then, unsafe groups are adjusted by inserting relaxed conservative users considering sensitivity requirements. Finally, segments covered by each group are published to protect location information. The efficiency and effectiveness of the method are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against sensitivity homogeneity attacks is small.