An efficient Multi-Keyword Synonym Ranked Query over Encrypted Cloud Data using BMS Tree

摘要

Cloud computing is embryonic technology as a new computing model in several business domains. Large numbers of large-scale organizations are starting to shift the information on to the cloud environment. However, shifting the data to the cloud relieves the organizations from the monotonous tasks of organization management, minimizes maintenance costs and also less hands on management. However, security and privacy become key security issues when data owners outsource their private data onto untrusted public cloud servers. However, traditional keyword plain text search is obsolete. Several techniques have been developed to preserve the privacy of sensitive data in the cloud environment. But existing searching techniques over encrypted cloud data considers only exact or fuzzy keyword or multi-keyword, but not synonym based ranking searching including multi-keyword. In this paper, we propose an efficient multi-keyword synonym query over encrypted cloud data by retrieving top k scored documents. The vector space model and TFIDF model are used to construct index and query generation. The KNN algorithm used to encrypt index and query vectors. We construct a special tree called Balanced M-way Search (BMS) Tree for indexing and propose a Depth First Search Technique (DFST) algorithm to achieve efficient multi-keyword synonym ranked search. The efficiency and accuracy of DFST algorithm are illustrated with an example, BMS tree, it takes sub-linear time complexity.