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A CAE model-based secure deduplication method.

Chunbo Wang1, Guoying Zhang1, Hui Qi1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

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|July 9, 2025
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This study introduces a novel secure cloud data deduplication method using Convolutional Autoencoder (CAE) models. The approach significantly enhances efficiency and reduces computational overhead compared to existing techniques.

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Area of Science:

  • Computer Science
  • Data Storage
  • Cloud Computing
  • Machine Learning
  • Cybersecurity

Background:

  • Cloud storage offers convenience but suffers from duplicate data, increasing storage burden and privacy risks.
  • Existing secure deduplication methods like Random Message Locked Encryption (R-MLE) face computational challenges due to bilinear mapping.

Purpose of the Study:

  • To develop a secure and efficient cloud data deduplication method.
  • To reduce the computational overhead associated with traditional secure deduplication techniques.
  • To improve data privacy and storage efficiency in cloud environments.

Main Methods:

  • Proposed a secure deduplication method utilizing an Autoencoder model to generate summary tags.
  • Introduced a Convolutional Autoencoder (CAE) model to further reduce parameters, computational, and storage overhead.
  • Employed convolution and pooling operations within the CAE for enhanced efficiency and overfitting mitigation.

Main Results:

  • The proposed Autoencoder and CAE-based methods significantly improve deduplication efficiency.
  • Reduced model storage requirements were observed with the CAE approach.
  • Experiments demonstrated a more uniform distribution of data, indicating better storage utilization.

Conclusions:

  • The CAE-based secure deduplication method offers superior performance over existing techniques.
  • This approach effectively addresses the storage burden and privacy concerns associated with duplicate cloud data.
  • The method provides a promising solution for efficient and secure cloud data management.