CSMRS: An Efficient and Effective Semantic-aware Ranked Search Scheme over Encrypted Cloud Data
Zheng Fei Hu, Hua Dai, Yuanlong Liu, Geng Yang, Qian Zhou, Yanli Chen · 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · 2022
The document vectors constructed by the traditional searchable encryption scheme based on the term frequency-inverse document frequency model not only have high dimensionality and sparsity, but also ignore the semantic information of documents and keywords. In this paper, we introduce the sentence bidirectional encoder representations from transformers model (SBERT) to obtain semantic information-embedded vectors for documents and keywords. By adopting the SBERT model, we pro-pose a CBG-index based semantic-aware multi-keyword ranked search scheme (CSMRS). In the scheme, a topic-term frequency-inverse topic frequency (TTF-ITF) model and a clustering-based group index (CBG-index) are proposed. The TTF-ITF model is used to generate semantic vectors for keywords, and the CBG-index is used to improve the search efficiency. The experimental results demonstrate the better performance than the existing works in terms of search efficiency and search result semantic precision.