Semantic and secure search over encrypted outsourcing cloud based on BERT

Abstract

Searchable encryption provides an effective way for data security and privacy in cloud storage. Users can retrieve encrypted data in the cloud under the premise of protecting their own data security and privacy. However, most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the text. In this paper, we propose two secure and semantic retrieval schemes based on BERT (bidirectional encoder representations from transformers) named SSRB-1, SSRB-2. By training the documents with BERT, the keyword vector is generated to contain more semantic information of the documents, which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s intention. Finally, through testing on real data sets, it is shown that both of our solutions are feasible and effective.

Graphical abstract

Keywords

cloud computing / semantic search / BERT model / searchable encryption

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