A Random Sensitive Area Based Privacy Preservation Algorithm for Location-Based Service

In general, generalization is a common strategy used for preserving the user's privacy in location-based service (LBS). In this strategy, at least k-1 anonymous users are selected to generalize the real location. However, in some special areas, there will be too many anonymous users to be selected in a limited space, and the real location can be correlated with the specified area and violates the privacy. Therefore, in this paper, in order to cope with problem mentioned above, a random sensitive area based privacy preservation algorithm is proposed. In this algorithm, before selecting anonymous users, several random sensitive areas are selected. Then based on the selected areas, anonymous users are selected to generalize the real location. With these operations, the real location is not only generalized by anonymous users but also generalized by sensitive areas with different types, so the adversary will be even more difficult to identify the real location. At last, security analysis as well as simulation experiments are given to further demonstrate the superiority of algorithm proposed in the level of privacy preservation and the capability of execution efficiency.

Automated summary

This paper proposes a privacy preservation algorithm based on random sensitive areas, which selects multiple random sensitive areas and anonymous users to generalize the real location, thereby increasing the difficulty for adversaries to identify the real location.