Protecting privacy for distance and rank based group nearest neighbor queries

Abstract

This paper proposes a novel approach to safeguarding location privacy for GNN (group nearest neighbor) queries. Given the locations of a group of dispersed users, the GNN query returns the location that minimizes the total or the maximal distance for all group users. The returned location is typically a meeting place such as a cinema or coffee shop where the group would like to meet. In our work, we highlight the challenges for private GNN queries and propose a general framework that have two key features: (i) it ensures privacy in a decentralized manner and (ii) can compute an optimal location for GNN query that maximizes the group’s overall preference for the meeting place. To mask their precise locations, we assume that user locations are given as regions to a location-based service provider (LSP). The LSP computes then a set of candidate answers (i.e., meeting places) for the GNN query. We identify two privacy attacks on the user locations, the distance intersection attack and the rank disclosure attack. These attacks are possible when the answer of a GNN query is determined from the candidate answers in a straightforward manner. We develop private filters that prevent these attacks and compute the GNN from the retrieved candidate answers. Our decentralized approach ensures that neither the users nor the LSP can learn the location of any group member. Our algorithms compute from the candidate set an optimal meeting place given the group members’ imprecise locations. Our key insight to an efficient computation is to prune the meeting places that cannot be GNNs given the locations of the group members within the search region. A comprehensive experimental evaluation shows the effectiveness of our approach to answering private GNN queries.