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EPVM: efficient and publicly verifiable computation for matrix multiplication with privacy preservation

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Abstract

With the rapid development of cloud computing, clients and users with limited computing resources can outsource their computation-intensive tasks to the Cloud Service Providers (CSPs). However, as the CSPs are commercial in nature and aim to increase their profits, some security challenges are still attached to them. In this paper, we propose an efficient publicly verifiable computation scheme (EPVM) for large-scale matrix multiplication with privacy preservation. Based on the theory of discrete logarithm problem and the techniques of privacy-preserving matrix transformation, our scheme not only protects the privacy of the client’s matrices but also significantly reduces the computation overhead on the client end as well as the CSP side. Our detailed security analysis and proofs show that the proposed scheme can achieve the established security requirements. The experimental evaluation also demonstrates that the proposed scheme works efficiently as compared to other existing solutions.

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Funding

This research is supported by the National Natural Science Foundation of China (Grant Nos. 62272042, 61972037).

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Authors and Affiliations

  1. School of Cyberspace Science & Technology, Beijing Institute of Technology, Beijing, China

    Chang Xu, Hongzhou Rao, Liehuang Zhu & Chuan Zhang

  2. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China

    Kashif Sharif

Authors
  1. Chang Xu
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  2. Hongzhou Rao
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  3. Liehuang Zhu
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  4. Chuan Zhang
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  5. Kashif Sharif
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Contributions

The authors in the list all contributed to the study conception and design. The main conception and design were proposed by Chang Xu, and completed with the help of Hongzhou Rao, Liehuang Zhu, and ChuanZhang. Chang Xu did the work of formal analysis. Hongzhou Rao wrote the first draft of the manuscript. And Kashif Sharif provided professional advice on reviewing and editing the manuscript. The experiments were designed by Chang Xu and Hongzhou Rao.

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Correspondence to Chang Xu.

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Xu, C., Rao, H., Zhu, L. et al. EPVM: efficient and publicly verifiable computation for matrix multiplication with privacy preservation. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04329-2

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