This paper proposed a multi-keyword ciphertext search, based on an improved-quality\nhierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to\nwork with encrypted data. It has improved search accuracy and can self-adapt when performing\nmulti-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document\nvectors are first generated by combining the term frequency-inverse document frequency (TF-IDF)\nweight factor and the vector space model (VSM). The improved quality hierarchical clustering\n(IQHC) algorithm then generates document vectors, document indices, and cluster indices, which\nare encrypted via the k-nearest neighbor algorithm (KNN). MCS-IQHC then returns the top-k\nsearch result. A series of experiments proved that the proposed method had better searching\nefficiency and accuracy in high-privacy sensor cloud network environments, compared to other\nstate-of-the-art methods.
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