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How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?

Author

Listed:
  • Yiming Zhu

    (College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou 510632, China)

  • Dehua Zhou

    (College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou 510632, China)

  • Yuan Li

    (College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou 510632, China)

  • Beibei Song

    (College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou 510632, China)

  • Chuansheng Wang

    (College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou 510632, China)

Abstract

Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications.

Suggested Citation

  • Yiming Zhu & Dehua Zhou & Yuan Li & Beibei Song & Chuansheng Wang, 2023. "How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?," Future Internet, MDPI, vol. 15(6), pages 1-21, May.
  • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:197-:d:1158817
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