A spatial transformation scheme supporting data privacy and query integrity for security of outsourced databases

Outsourcing database to a third-party data provider is becoming a common practice for data owners to avoid the cost of managing and maintaining the database. Meanwhile, because of the popularity of location-based services, the need for spatial data is increasing dramatically. However, the most important challenge in database outsourcing is how to meet privacy requirements and guarantee the integrity of the query result as well. Unfortunately, most of the existing techniques support either data privacy or integrity on spatial databases. To carry on both privacy and integrity for outsourced spatial data, we propose a spatial transformation scheme that makes use of shearing transformation with rotation shifting. We describe attack models measuring the data privacy of our transformation scheme. Finally, our extensive experiments have demonstrated that our scheme has adequate strength for data privacy by showing outstanding performance against different kinds of attack models and efficiently handles the query integrity of the query result sets.