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<p>Community data sharing is vital for resource optimization and smart decision-making. However, existing schemes face two key issues: balancing multi-keyword ranked search with fine-grained access control, and ensuring reliable verification of search results in distributed environments to maintain data consistency and user trust. Based on this, the paper introduces a specialized multi-keyword rank searchable encryption scheme, <i>MRAP-SE</i>, designed for community data sharing within a peer-to-peer (P2P) architecture. It adeptly addresses critical concerns such as privacy protection in data access and authorization, along with enhancing the accuracy and credibility of search results. Central to <i>MRAP-SE</i> is the implementation of the vector space model, which significantly improves search precision. The scheme further ensures the reliability of search outcomes through a robust multi-node retrieval and signature mechanism, enhancing the overall security and trustworthiness. Additionally, <i>MRAP-SE</i> incorporates the linear secret sharing scheme for concealing user attributes and authorization policies, facilitating fine-grained and efficient data authorization strategies. To tackle the lack of effective incentive mechanisms in P2P frameworks, this paper also proposes a points-based incentive, aimed at boosting participation and sustainability in community data sharing. The paper concludes with a comprehensive security analysis and performance evaluation, demonstrating the scheme’s effectiveness in ensuring robust security and efficiency in community data sharing environments.</p>

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Incentive-assisted multi-keyword ranked searchable encryption with hidden attributes for community data sharing

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • Jingting Xue,
  • Lusha Zeng,
  • Wenzheng Zhang,
  • Fagen Li,
  • Xiaojun Zhang,
  • Yu Zhou,
  • Liang Liu

摘要

Community data sharing is vital for resource optimization and smart decision-making. However, existing schemes face two key issues: balancing multi-keyword ranked search with fine-grained access control, and ensuring reliable verification of search results in distributed environments to maintain data consistency and user trust. Based on this, the paper introduces a specialized multi-keyword rank searchable encryption scheme, MRAP-SE, designed for community data sharing within a peer-to-peer (P2P) architecture. It adeptly addresses critical concerns such as privacy protection in data access and authorization, along with enhancing the accuracy and credibility of search results. Central to MRAP-SE is the implementation of the vector space model, which significantly improves search precision. The scheme further ensures the reliability of search outcomes through a robust multi-node retrieval and signature mechanism, enhancing the overall security and trustworthiness. Additionally, MRAP-SE incorporates the linear secret sharing scheme for concealing user attributes and authorization policies, facilitating fine-grained and efficient data authorization strategies. To tackle the lack of effective incentive mechanisms in P2P frameworks, this paper also proposes a points-based incentive, aimed at boosting participation and sustainability in community data sharing. The paper concludes with a comprehensive security analysis and performance evaluation, demonstrating the scheme’s effectiveness in ensuring robust security and efficiency in community data sharing environments.