Efficient secure and verifiable KNN set similarity search over outsourced clouds

Abstract

KNN set similarity search is a foundational operation in various realistic applications in cloud computing. However, for security consideration, sensitive data will always be encrypted before uploading to the cloud servers, which makes the search processing a challenging task. In this paper, we focus on the problem of KNN set similarity search over the encrypted datasets. We use Yao’s garbled circuits and secret sharing as underlying tools. To achieve better querying efficiency, we construct a secure R-Tree index structure based on a novel secure grouping protocol, which enables grouping appropriate private values in an oblivious way. Along with several elaborately designed secure arithmetic subroutines, we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds. Theoretically, we analyze the complexity of our schemes in detail, and prove the security in the presence of semi-honest adversaries. Finally, we evaluate the performance and feasibility of our proposed methods by extensive experiments.