A Novel Feature Matching Ranked Search Mechanism Over Encrypted Cloud Data

Abstract

Encrypted search technology has been studied extensively in recent years. With more and more information being stored in cloud, creating indexes with independent keywords has resulted in enormous storage cost and low search accuracy, which has become an urgent problem to be solved. Thus, in this paper, we propose a new feature matching ranked search mechanism (FMRSM) for encrypted cloud data. This mechanism uses feature score algorithm (FSA) to create indexes, which allows multi-keywords which are extracted from a document as a feature to be mapped to one dimension of the index. Thus, the storage cost of indexes can be reduced and the efficiency of encryption can be improved. Moreover, FMRSM uses a matching score algorithm (MSA) in generating trapdoor process. With the help of FSA, the matching score algorithm can rank the search results according to the type of match and the number of matching keywords, and therefore it is able to return results with higher ranking accuracy. Comprehensive analysis prove that our mechanism is more feasible and effective.

Keywords

• Encrypted search
• feature score
• storage cost
• matching score
• ranking accuracy