A k-nearest neighbor search algorithm for enhancing data privacy in outsourced spatial databases

With the advancement of cloud computing technologies and the propagation of location-based services, research on outsourced spatial databases has been spotlighted. Therefore, the traditional spatial databases owners want to outsource their resources to a service provider 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 query processing algorithms encrypt spatial database and perform a query on encrypted data. Nevertheless, the existing 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 k-NN query processing algorithm outperforms the existing algorithm in terms of query processing time and the size of candidate result.