Central keyword ciphertext searching method base on two-level index in cloud environment

Abstract

The invention discloses a central keyword ciphertext searching method base on a two-level index in a cloud environment. The method comprises the following steps that (1) a data owner generates a key;(2) the data owner constructs and encrypts a reverse index; (3) the data owner constructs and encrypts a forward index; (4) a data user generates a central keyword tag; (5) the data user generates a query trap door; (6) a cloud server executes the two-step search. The method achieves the central keyword search, the search efficiency is high, and the search accuracy is high.

Description

technical field [0001] The invention relates to cloud security technology, in particular to a central keyword ciphertext search method based on a two-level index in a cloud environment. Background technique [0002] In recent years, cloud computing technology has attracted more and more individual users or business users to rent cloud storage services with its high-quality and convenient services, which has led to rapid growth of cloud data. Since cloud service providers are mostly commercial private enterprises, while providing services, they may steal user privacy for some commercial purposes. Therefore, we must be aware of the security threats to user data privacy in the cloud storage environment. Privacy data encryption is an important means to prevent privacy leakage, but it makes the search based on keywords a challenge, so research on searchable encryption technology has become an urgent practical need. [0003] Searchable encryption technology is an important resea...

AI Technical Summary

Problems solved by technology

[0005] (1) It is not ensured that the central keyword must be included in the search results. Although the weight of the central keyword is emphasized and the ranking of the search results is optimized, there will be cases where other keywords are included in the search results but not the central keyword. ;

[0006] (2) A search needs to traverse all index vectors, and calculate the inner product of each index vector and query vector separately, which takes a lot of time

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