Enhanced Privacy Preserving Group Nearest Neighbor Search

Abstract

Group $k$ k -nearest neighbor ( $k$ k GNN) search allows a group of $n$ n mobile users to jointly retrieve $k$ k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving $k$ k GNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving $k$ k GNN search in the full user collusion environment, where any $n-1$ n - 1 users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider $k$ k GNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., $k$ k GNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.