Multikeyword Ranked Search Based on Hierarchical Clustering Index

Abstract

Outsourcing of data into cloud becomes an effective trend in modern day computing due to its ability to provide low-cost, pay-as-you-go IT services. Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore, it is necessary to develop effective and reliable cipher text search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design cipher text search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is planned to support more search semantics and also to meet the demand for fast cipher text search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In order to confirm the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the implemented method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.