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A k-Nearest Neighbor Search Algorithm for Privacy Preservation in Outsourced Spatial Databases

Traditional spatial databases owners outsource their resources to a cloud computing environment so that they can reduce cost for storage and management. However, the issue of privacy preservation is crucial in spatial database outsourcing since user location data is sensitive against unauthorized accesses. Existing privacy-preserving algorithms may reveal the original database from encrypted database and the query processing algorithms fall short in offering query processing on road networks. In this paper, we propose a privacy-preserving query processing algorithm which performs on encrypted spatial database. A new node-anchor index is designed to reduce unnecessary network expansions for retrieving k-nearest neighbor (k-NN) objects from a query point. Performance analysis shows that our algorithm outperforms the existing algorithm in terms of query processing time and the result size.