With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to\nthe fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is\nexposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware\nfailure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate\nverifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering,\nwhich is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which\nis based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based\non the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and\nthus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query\nvector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the\nproposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.
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