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A novel semantic-aware search scheme based on BCI-tree index over encrypted cloud data

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Abstract

Most of the traditional privacy-preserving search schemes in cloud adopt TF-IDF model which is on the basis of keyword frequency statistics. The embedding semantic association between keywords and documents are not considered. To solve this problem, we propose a novel semantic-aware search scheme based on BCI-tree index over encrypted cloud data. The LDA model is adopted to generate vectors for documents and queried keywords and the vectors contain topic-based semantic information. The homomorphic encryption on vectors is used to perform privacy-preserving semantic relevance score computation between queried keywords and documents. To achieve efficient search processing, a novel binary clustering tree-based index (BCI-tree index) is designed, which is constructed following the divisive hierarchical clustering algorithm. By using the BCI-tree index, a depth-first recursive search algorithm is proposed. In addition, a threshold presetting-based optimization is applied to further accelerate the search speed. The experimental results show that the proposed scheme performs good in the semantic precision of search results and the search time cost.

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Details of the datasets have been described in Section 8.

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Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China under the grant Nos.61902199, 61872197,and 61972209; and the Jiangsu Province Postgraduate Scientific Research Innovation Program under the grand No. KYCX22_0984.

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Authors and Affiliations

  1. School of Modern Posts, Nanjing University of Posts and Telecommunications, 210023, Nanjing, China

    Qian Zhou

  2. School of Computer Science, Nanjing University of Posts and Telecommunications, 210023, Nanjing, China

    Hua Dai, Yuanlong Liu, Geng Yang & Zheng Hu

  3. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, 210023, Nanjing, China

    Hua Dai & Geng Yang

  4. School of Computing Technologies, RMIT University, 3001, Melbourne, Australia

    Xun Yi

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  1. Qian Zhou
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  2. Hua Dai
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Contributions

This work thanks the following authors for their contributions: Qian Zhou and Hua Dai contributed to the conception of the study; Qian Zhou and Yuanlong Liu performed the experiment; Qian Zhou and Zheng Hu contributed significantly to security analysis and manuscript preparation; Qian Zhou and Hua Dai performed the data analyses and wrote the manuscript; Geng Yang and Xun Yi helped perform the analysis with constructive discussions.

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Correspondence to Qian Zhou.

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Zhou, Q., Dai, H., Liu, Y. et al. A novel semantic-aware search scheme based on BCI-tree index over encrypted cloud data. World Wide Web 26, 3055–3079 (2023). https://doi.org/10.1007/s11280-023-01176-w

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