Authenticating location-based services without compromising location privacy
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Secure k-NN computation on encrypted cloud data without sharing key with query users
Proceedings of the 2013 international workshop on Security in cloud computing
Private proximity detection and monitoring with vicinity regions
Proceedings of the 12th International ACM Workshop on Data Engineering for Wireless and Mobile Acess
Secure k-NN query on encrypted cloud database without key-sharing
International Journal of Electronic Security and Digital Forensics
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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.