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Prior-based privacy-assured compressed sensing scheme in cloud

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Abstract

Compressed sensing (CS) is a popular signal processing technique. However, some of its performances still need to be improved for possible secure visual applications, including the optimization of the measurement matrix, privacy assurance, and the sparse recovery performance. To this end, we present prior-based measurement matrix design and sparse recovery algorithm for privacy-assured CS scheme in the cloud. More specifically, the measurement matrix is modeled by minimizing a Frobenius difference between the identity matrix and the Gram of the weighted sensing matrix. The gradient descent method is employed to derive the prior probability-weighted measurement matrix. Further, privacy-assured CS can be achieved by using within-row permutation and chaotic matrices. Finally, we also employ the prior information to enhance the accuracy of the sparse recovery algorithm by using prior probability-weighted orthogonal matching pursuit. Theoretical analyses and simulation results demonstrate that the proposed scheme can optimize measurement matrix, achieve privacy-assured CS, and improve sparse recovery performance.

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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

The work was supported by the National Key R &D Program of China (Grant No. 2020YFB1805400), the National Natural Science Foundation of China (Grant No. 62072063), and the Project Supported by Graduate Student Research and Innovation Foundation of Chongqing, China (Grant No. CYB 21062).

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  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Hui Huang, Di **ao, Jia Liang & Min Li

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  1. Hui Huang
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  2. Di **ao
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Correspondence to Di **ao.

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Huang, H., **ao, D., Liang, J. et al. Prior-based privacy-assured compressed sensing scheme in cloud. Vis Comput 40, 2103–2117 (2024). https://doi.org/10.1007/s00371-023-02906-x

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