Keyword Semantic Extended Top-k Ciphertext Retrieval Scheme Over Hybrid Government Cloud Environment

Abstract

Privacy protection is a key problem that must be solved when building government cloud system to realize data storage and sharing. In order to meet the privacy protection requirements of data security storage and document security sharing between data publishers and authorized data access users over the government cloud platform, this paper proposes a keyword semantic extended Top-k ciphertext retrieval scheme over hybrid government cloud environment. Firstly, the scheme uses the hybrid cloud mode to build the government cloud platform to realize the storage and sharing of government documents. Then, the key technologies such as mechanical segmentation method, term frequency-inverse document frequency statistics method, keyword semantic expansion method, homomorphic matrix encryption method and vector space model are used to retrieve the ciphertext documents in a completely confidential state. According to the correlation score calculated by ciphertext retrieval, the Top-k ciphertext target documents with the highest correlation score are returned. And then the scheme decrypts and restores to obtain the Top-k plaintext target documents. The analysis of security and experimental test results shows that this scheme can not only meet the data storage and sharing requirements over government cloud environment, but also prevent the privacy leakage risk of data over government cloud. It is an effective solution to promote the construction and development of government cloud.

Keywords

• Hybrid government cloud
• semantic extension
• correlation score
• ciphertext retrieval
• privacy protection