Secure KNN Set Similarity Search in Outsourced Cloud Environments

Abstract

With the boom in cloud computing, it is a promising choice for data owners to outsource their data to the cloud server, which can large scale data storage, management, and query processing. Nevertheless, the cloud server is untrusted and it may capture or infer the sensitive information, such as medical or financial records, which necessitate them to be encrypted before being outsourced for privacy concerns. In this paper, we propose a secure KNN set similarity search in outsourced cloud environments by Yao's garbled circuits which can preserve the data privacy for both data owner and the user. To support this framework, we design a novel unified structure, called secure R-tree circuit index, and propose a scheme to achieve completely secret grouping in garbled circuits. Based on the above, we design a series of secure arithmetic sub-protocols to facilitate KNN set similarity query process efficiently. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.