Achieve personalized anonymity through query blocks exchanging

In cyberspace security, the privacy in location-based services (LBSs) becomes more critical. In previous solutions, a trusted third party (TTP) was usually employed to provide disturbance or obfuscation, but it may become the single point of failure or service bottleneck. In order to cope with this drawback, we focus on another important class, establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms, we found users in the group must share the same maximum anonymity degree, and they could not ease the process of preservation in a lower one. To cope with this problem, we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms, and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally, experimental evaluations further verify its effectiveness and efficiency.