Visible to the public Authentication of k Nearest Neighbor Query on Road Networks

TitleAuthentication of k Nearest Neighbor Query on Road Networks
Publication TypeJournal Article
Year of Publication2014
AuthorsYinan Jing, Ling Hu, Wei-Shinn Ku, Shahabi, C.
JournalKnowledge and Data Engineering, IEEE Transactions on
Volume26
Pagination1494-1506
Date PublishedJune
ISSN1041-4347
KeywordsArtificial neural networks, authentication, computational geometry, database outsourcing paradigm, Euclidean space, Generators, Google Android mobile devices, k nearest neighbor query, location-based services, outsourcing, query authentication, query processing, road network k-nearest-neighbor query verification technique, road networks, Roads, smart phones, Spatial database outsourcing, Spatial databases, spatial query integrity, third-party service provider, visual databases, Voronoi diagram
Abstract

Outsourcing spatial databases to the cloud provides an economical and flexible way for data owners to deliver spatial data to users of location-based services. However, in the database outsourcing paradigm, the third-party service provider is not always trustworthy, therefore, ensuring spatial query integrity is critical. In this paper, we propose an efficient road network k-nearest-neighbor query verification technique which utilizes the network Voronoi diagram and neighbors to prove the integrity of query results. Unlike previous work that verifies k-nearest-neighbor results in the Euclidean space, our approach needs to verify both the distances and the shortest paths from the query point to its kNN results on the road network. We evaluate our approach on real-world road networks together with both real and synthetic points of interest datasets. Our experiments run on Google Android mobile devices which communicate with the service provider through wireless connections. The experiment results show that our approach leads to compact verification objects (VO) and the verification algorithm on mobile devices is efficient, especially for queries with low selectivity.

URLhttp://ieeexplore.ieee.org/document/6658750/
DOI10.1109/TKDE.2013.174
Citation Key6658750