Processing Private Queries Over Untrusted Data Cloud Through Privacy Homomorphism
Query processing that preserves both the data privacy of the owner and the query privacy of the client is a new research problem. It shows increasing importance as cloud computing drives more businesses to outsource their data and querying services. However, most existing studies, including those on data outsourcing, address the data privacy and query privacy separately and cannot be applied to this problem. In this paper, the authors propose a holistic and efficient solution that comprises a secure traversal framework and an encryption scheme based on privacy homomorphism. The framework is scalable to large datasets by leveraging an index-based approach. Based on this framework, they devise secure protocols for processing typical queries such as k-Nearest-Neighbor queries (kNN) on R-tree index.