Abstract
This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document vectors are first generated by combining the term frequency-inverse document frequency (TF-IDF) weight factor and the vector space model (VSM). The improved quality hierarchical clustering (IQHC) algorithm then generates document vectors, document indices, and cluster indices, which are encrypted via the k-nearest neighbor algorithm (KNN). MCS-IQHC then returns the top-k search result. A series of experiments proved that the proposed method had better searching efficiency and accuracy in high-privacy sensor cloud network environments, compared to other state-of-the-art methods.
Highlights
Recent advancements in sensor networks, big data, and cloud computing technologies have placed increasingly stringent requirements on the transfer and outsourcing of sensor data to cloud servers [1,2]
Cloud servers are semi-credible; privacy protection is a crucial aspect of their functionality; and a ciphertext search method is currently the search method of choice most commonly deployed in cloud environments
We propose a method for building document vectors, by combing term frequency-inverse document frequency (TF-IDF) with Vector Space Model (VSM) to optimize search efficiency, and we propose an improved quality hierarchical clustering (IQHC) algorithm based on the quality hierarchical clustering algorithm (QHC)
Summary
Recent advancements in sensor networks, big data, and cloud computing technologies have placed increasingly stringent requirements on the transfer and outsourcing of sensor data to cloud servers [1,2]. Wang [6] established a technique that sorted document keywords based on term frequency-inverse document frequency (TF-IDF) Though this approach reduced search time, it required specific encryption search software, which increased the computational complexity of the system. We propose a method for building document vectors, by combing term frequency-inverse document frequency (TF-IDF) with Vector Space Model (VSM) to optimize search efficiency, and we propose an improved quality hierarchical clustering (IQHC) algorithm based on the quality hierarchical clustering algorithm (QHC) We incorporated this improved algorithm into a new multi-keyword ranked search method for encrypted sensor data and used the new search method to implement a multi-keyword ciphertext search over encrypted cloud data, based on the improved quality hierarchical clustering algorithm (MCS-IQHC).
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