Open Access

ARTICLE

Privacy-Preserving Content-Aware Search Based on Two-Level Index

Zhangjie Fu^1,*, Lili Xia¹, Yuling Liu², Zuwei Tian³

1 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
2 College of Computer Science and Electronic Engineering, Hunan University, China
3 School of Information Science and Engineering, Hunan First Normal University, Hunan, China.

* Corresponding Author: Zhangjie Fu. Email: .

Computers, Materials & Continua 2019, 59(2), 473-491. https://doi.org/10.32604/cmc.2019.03785

Abstract

Nowadays, cloud computing is used more and more widely, more and more people prefer to using cloud server to store data. So, how to encrypt the data efficiently is an important problem. The search efficiency of existed search schemes decreases as the index increases. For solving this problem, we build the two-level index. Simultaneously, for improving the semantic information, the central word expansion is combined. The purpose of privacy-preserving content-aware search by using the two-level index (CKESS) is that the first matching is performed by using the extended central words, then calculate the similarity between the trapdoor and the secondary index, finally return the results in turn. Through experiments and analysis, it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.

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Citations