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<p>With the rapid advancement of location-based services, the effective management and utilization of massive spatio-textual data are receiving increasing attention. Due to the limited storage and computing capability of local users, the users usually encrypt their spatio-textual database and outsource to the cloud. In recent years, privacy-preserving spatial keyword query schemes for encrypted spatio-textual database have been developed, aiming to retrieve encrypted spatial objects of interest to users from the cloud. In this paper, we propose a novel query approach: Privacy-preserving Top-<i>k</i> Spatial keyword Similarity query with Priority matching (PTSSP), and give a practical scheme. Our scheme focuses on returning the top-<i>k</i> spatial objects with the largest keyword weight within a given range and whose similarity to the query keywords is greater than a given threshold. We employ the cosine similarity to measure the similarity between the query keywords and the keywords contained in the objects. We convert keywords and keyword weights to vectors. We employ the enhanced asymmetric scalar-product-preserving encryption algorithm to encrypt the vectors, which can perform inner product operation on the ciphertext matrices to obtain the operation result of the plaintext matrices without leaking any information of the plaintexts. To improve the search efficiency, we design a tree-based secure index structure to realize the sublinear search. We conclude with an extensive security analysis of the proposed scheme, demonstrating its resistance to indistinguishable chosen plaintext attacks. We conduct the experiments on real spatial database for our proposed scheme and the typical schemes, which show our scheme performs better than these schemes.</p>

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PTSSP: privacy-preserving top-k spatial keyword similarity query with priority matching

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Shen Du,
  • Xinrui Ge

摘要

With the rapid advancement of location-based services, the effective management and utilization of massive spatio-textual data are receiving increasing attention. Due to the limited storage and computing capability of local users, the users usually encrypt their spatio-textual database and outsource to the cloud. In recent years, privacy-preserving spatial keyword query schemes for encrypted spatio-textual database have been developed, aiming to retrieve encrypted spatial objects of interest to users from the cloud. In this paper, we propose a novel query approach: Privacy-preserving Top-k Spatial keyword Similarity query with Priority matching (PTSSP), and give a practical scheme. Our scheme focuses on returning the top-k spatial objects with the largest keyword weight within a given range and whose similarity to the query keywords is greater than a given threshold. We employ the cosine similarity to measure the similarity between the query keywords and the keywords contained in the objects. We convert keywords and keyword weights to vectors. We employ the enhanced asymmetric scalar-product-preserving encryption algorithm to encrypt the vectors, which can perform inner product operation on the ciphertext matrices to obtain the operation result of the plaintext matrices without leaking any information of the plaintexts. To improve the search efficiency, we design a tree-based secure index structure to realize the sublinear search. We conclude with an extensive security analysis of the proposed scheme, demonstrating its resistance to indistinguishable chosen plaintext attacks. We conduct the experiments on real spatial database for our proposed scheme and the typical schemes, which show our scheme performs better than these schemes.