Traditional searchable symmetric encryption (SSE) schemes rarely support context-aware semantic extension, and then lead to the searched results being incomplete or deviating from the user’s query intention. To address this problem, a new context-aware semantically extensible searchable symmetric encryption based on Word2vec model (CASE-SSE) is proposed to achieve context-aware semantic extension in this article. The proposed scheme utilizes outsourced datasets as corpora to extract all keywords for training the Word2vec model, and the trained results is the ontology knowledge base that can be used to extend the semantics of query keywords directly. Further, to facilitate multi-keyword search using the extended query vector, we use the k -means clustering algorithm to classify outsourced datasets. We then construct an AVL-tree index and an inverted index based on the classified results, thereby achieving efficient context-aware semantically extensible SSE. The security analysis indicates it is secure and effective. The experimental results show that our scheme is superior in both efficiency and accuracy.

中文翻译：

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CASE-SSE：加密云数据的上下文感知语义可扩展可搜索对称加密


传统的可搜索对称加密（SSE）方案很少支持上下文感知的语义扩展，从而导致搜索结果不完整或偏离用户的查询意图。针对这一问题，本文提出了一种基于Word2vec模型的上下文感知语义可扩展可搜索对称加密（CASE-SSE）来实现上下文感知语义扩展。该方案利用外包数据集作为语料库提取所有关键词来训练Word2vec模型，训练结果是本体知识库，可直接用于扩展查询关键词的语义。此外，为了使用扩展查询向量促进多关键字搜索，我们使用 k 均值聚类算法对外包数据集进行分类。然后，我们根据分类结果构建 AVL 树索引和倒排索引，从而实现高效的上下文感知语义可扩展 SSE。安全分析表明该方案安全、有效。实验结果表明，我们的方案在效率和准确性上都具有优越性。