Enhancing Confidentiality and Privacy of Outsourced Spatial Data

Abstract

The increase of spatial data has led organizations to upload their data onto third-party service providers. Cloud computing allows data owners to outsource their databases, eliminating the need for costly storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties as well as providing efficient and accurate query results to the authenticated users. We propose a dual transformation scheme on the spatial database to overcome this problem, while the service provider executes queries and returns results to the users. First, our approach utilizes the space-filling Hilbert curve to map each spatial point in the multidimensional space to a one-dimensional space. This space transformation method is easy to compute and preserves the spatial proximity. Next, the order-preserving encryption algorithm is applied to the clustered data. The user issues spatial range queries to the service provider on the encrypted Hilbert index and then uses a secret key to decrypt the query response returned. This allows data protection and reduces the query communication cost between the user and service provider.