A tree based location privacy approach against multi-precision continuous attacks in the internet of things

Smart objects may build k-anonymity groups collaboratively to enhance location privacy in the Internet of Things environments, but former spatial cloaking approaches are vulnerable to multi-precision continuous attacks. In this paper, we propose a location query approach based on an anonymity-tree supporting multi-precision queries, in which an improved trilateration method is used to identify objects' relative location on the physical layer, an anonymity-tree is maintained for topology stability and query efficiency on the network layer. During location query processes smart objects generates new groups on the tree and returns the corresponding cloaked region according to queriers' identities. Cached information including tree topology and group status limits communication of group setup and member query to some subtrees, thereby network and time overhead are both reduced. The simulations show that the tree-based approach is efficient in a dynamic network, time overhead of the tree-based approach is comparable to the native spatial approach while its ability of resisting multi-precision continuous attack is much better. 1548-7741/