Secure kNN queries over outsourced spatial data for location-based services

Abstract

Cloud computing allows data owners to outsource their databases, eliminating the need for costly storage and computational resources. The key is to maintain data confidentiality with respect to third-party service providers along with providing query results in real-time to authorized users. In this paper, a transformation and encryption approach is proposed for spatial databases, where the service provider executes queries and returns results to the users. First, the approach uses the space-filling Hilbert curve to map each spatial point in the multidimensional space to a one-dimensional space. Next, the order-preserving encryption technique is applied to the transformed spatial data. The user issues spatial kNN queries to the service provider based on the Hilbert values and then uses the encryption key to decrypt the query response returned. Experiments are conducted to show that this approach reduces the query communication cost between the authorized user and the service provider.