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

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.