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Multimodal data privacy protection and completeness verification method for mobile crowd sensing

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Abstract

Most privacy-preserving approaches for mobile crowd sensing systems consider the privacy of single-modal data, while the data that sensing equipment may be sensing is frequently multimodal. Therefore, this paper proposes a multimodal data privacy protection and completeness verification method for mobile crowd sensing. Firstly, a cross-attention mechanism is utilized to fusion multimodal data to facilitate access to data information across various modes, improving the accuracy and reliability of data encryption. Secondly, a scalable superincreasing sequence is applied to store the multimodal data gathered by each sensing user, and the multimodal data is encrypted using an upgraded Paillier algorithm to prevent malicious attackers from obtaining the data information. Then, each ciphertext provides a validator using the Boneh-Lynn-Shacham signature algorithm. The sensing platform can apply a verification code to validate the completeness of the aggregated ciphertext data to ensure that the encrypted multimodal data has not been changed before being decrypted. Finally, experimental results demonstrate that the method proposed in this article not only effectively protects the privacy of multimodal data but also minimizes Communication costs and computational overhead.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

All the authors listed have approved the manuscript that is enclosed.

Funding

This present research work was supported by the National Natural Science Foundation of China (61403109, 61202458), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007) and the Heilongjiang Natural Science Foundation (LH2020F034).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Jian Wang, Fanfan Meng & Jia Liu

  2. College of Bioinformatics, Science and Technology, Harbin Medical University, Harbin, 150081, China

    Guanzhi He

  3. College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

    Guosheng Zhao

Authors
  1. Jian Wang
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  2. Fanfan Meng
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  3. Jia Liu
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  4. Guanzhi He
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  5. Guosheng Zhao
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Contributions

Jian Wang, Fanfan Meng, Jia Liu, Guanzhi He and Guosheng Zhao wrote and revised the manuscript together.

Corresponding authors

Correspondence to Jian Wang or Guanzhi He.

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I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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We would like to submit the manuscript entitled “Multimodal Data Privacy Protection and Completeness Verification Method for Mobile Crowd Sensing”, which we wish to be considered for publication in “Peer-to-Peer Networking and Applications”.

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Wang, J., Meng, F., Liu, J. et al. Multimodal data privacy protection and completeness verification method for mobile crowd sensing. Peer-to-Peer Netw. Appl. 18, 39 (2025). https://doi.org/10.1007/s12083-024-01850-4

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