Organizations have come to realize that storing their databases in the Cloud rather than in-house data centers is cheaper and more flexible. However, companies are still concerned about the privacy and the security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the entire dataset, which is impractical. This paper proposes a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. Data objects are encrypted and sorted using Z-order space-filling curve. An index is built on top of the encrypted data to be utilized by the Service Provider to identify and retrieve a superset of data objects that contains the answers to the query. Many simulation experiments have been carried out to measure the performance of the proposed scheme in terms of the redundancy in data retrieved. The experimental results show that the proposed scheme outperforms the most recent scheme by Kim et al. in terms of data redundancy.
Starling: A Scalable Query Engine on Cloud Functions