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<p>Electronic medical systems can alleviate system pressure by outsourcing patients’ encrypted electronic medical records to cloud servers. Therefore, achieving efficient search over encrypted data is crucial.Attribute-Based Keyword Search (ABKS) provides a mechanism for fine-grained access control while enabling keyword-based searches over encrypted medical records. However, existing ABKS schemes often suffer from search efficiency issues, as their retrieval time grows linearly with the size of the encrypted record. This scalability limitation poses challenges for the widespread adoption of ABKS in medical data sharing environments. This paper proposes a privacy-preserving Attribute-Based Multi-Keyword Search scheme(PAKS) for data sharing, which achieves fine-grained access control and efficient search. This scheme utilizes Bloom filters to construct a tree-based index, enhancing user search efficiency and achieving sublinear search. Additionally, the SKNN technique is introduced during the encryption and search phases to process keywords, allows the cloud server to retrieve the corresponding encrypted documents by computing relevance scores when a user submits a multi-keyword search, without exposing ciphertext information to the cloud server. Finally, we demonstrate the security of PAKS through a security analysis and experiments show that PAKS achieves better efficiency in scenarios with a large number of documents.</p>

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Privacy-preserving attribute-based multi-keyword search in e-health system

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Xiaohui Yang,
  • Shouhui Chen,
  • Bingzhi Tong

摘要

Electronic medical systems can alleviate system pressure by outsourcing patients’ encrypted electronic medical records to cloud servers. Therefore, achieving efficient search over encrypted data is crucial.Attribute-Based Keyword Search (ABKS) provides a mechanism for fine-grained access control while enabling keyword-based searches over encrypted medical records. However, existing ABKS schemes often suffer from search efficiency issues, as their retrieval time grows linearly with the size of the encrypted record. This scalability limitation poses challenges for the widespread adoption of ABKS in medical data sharing environments. This paper proposes a privacy-preserving Attribute-Based Multi-Keyword Search scheme(PAKS) for data sharing, which achieves fine-grained access control and efficient search. This scheme utilizes Bloom filters to construct a tree-based index, enhancing user search efficiency and achieving sublinear search. Additionally, the SKNN technique is introduced during the encryption and search phases to process keywords, allows the cloud server to retrieve the corresponding encrypted documents by computing relevance scores when a user submits a multi-keyword search, without exposing ciphertext information to the cloud server. Finally, we demonstrate the security of PAKS through a security analysis and experiments show that PAKS achieves better efficiency in scenarios with a large number of documents.