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An efficient position-sensitive fuzzy keyword search scheme for encrypted data on hybrid cloud

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Abstract

With the growing demand for effective and privacy-preserving data retrieval, searchable encryption has emerged as a promising solution for searching encrypted data stored on remote servers. Compared with exact keyword search, fuzzy keyword search can address misspellings and formatting inconsistencies, thus greatly enhancing the user’s search experience. However, existing fuzzy keyword search schemes tend to ignore the effect of letter position on the sorting of search results, resulting in redundant search results. To address this problem, a novel fuzzy sorting search scheme is proposed to efficiently search encrypted cloud data, which enables sorting while searching. The proposed scheme improves the sorting efficiency while increasing the search accuracy. The proposed scheme considers the alphabetical position of the keywords and uses the Euclidean distance metric to measure the similarity between search keywords and document keywords. To decrease the storage space for indexes, the proposed scheme build a binary index tree and design a tree-based search algorithm based on letter vectors and thresholds. To improve search efficiency, the proposed scheme combines keyword weight and keyword similarity to calculate similarity scores and uses dual servers to search encrypted cloud data. Finally, extensive analysis and experiments certify the effectiveness, efficiency, and accuracy of the proposed scheme, demonstrating its ability to meet the need for security, efficiency, and accuracy.

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No datasets were generated or analysed during the current study.

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Acknowledgements

This work was supported in part by the Education Department of Shandong Province, the Natural Science Foundation of Shandong Province under Grant ZR2022QF102 and the Shandong Provinceence Youth Innovation and Technology Program Innovation Team under Grant 2022KJ296.

Funding

The funding was provided by the Education Department of Shandong Province, the Natural Science Foundation of Shandong Province under Grant ZR2022QF102 and the Shandong Provinceence Youth Innovation and Technology Program Innovation Team under Grant 2022KJ296.

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Author notes

  1. Nan Li and Qianqian Su have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Technology, Qingdao University, Qingdao, 266071, Shandong, China

    Nan Li & Qianqian Su

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  1. Nan Li
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Contributions

N.L.: Investigation, Writing-original draft, Formal analysis, Validation. Q.S.: Conceptualization, Methodology, Writing-review and editing. All the authors reviewed the manuscript.

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Correspondence to Qianqian Su.

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Li, N., Su, Q. An efficient position-sensitive fuzzy keyword search scheme for encrypted data on hybrid cloud. Cluster Comput 28, 9 (2025). https://doi.org/10.1007/s10586-024-04703-0

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