Quality Hierarchical Cluster Algorithm to Verify Search Patterns in Cloud Data

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Quality Hierarchical Cluster Algorithm to Verify Search Patterns in Cloud Data

Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 8

Abstract

Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable cipher text search techniques. This paper challenges 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 proposed 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 subclusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set available. 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 proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.The Cloud storage is illustrated as a working model in school for Payroll management and Appraisal form.

Authors and Affiliations

Dr. P. Julia Grace, T. P. Padmini

Keywords

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EP ID EP392036
DOI 10.9790/9622-0708041317.
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