Department of Computer Science
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, we 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, we devise secure protocols for processing typical queries such as k-nearest-neighbor queries (kNN) on R-tree index. Moreover, several optimization techniques are presented to improve the efficiency of the query processing protocols. Our solution is verified by both theoretical analysis and performance study. © 2011 IEEE.
Source Publication Title
The 2011 IEEE 27th International Conference on Data Engineering
Hu, Haibo, Jianliang Xu, Chushi Ren, and Byron Choi. "Processing private queries over untrusted data cloud through privacy homomorphism." The 2011 IEEE 27th International Conference on Data Engineering (2011): 601-612.