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Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution.

Shimiao Li1, Pengzhi Yin2, Zehao Zhou3

  • 1School of Physics and Electronics, Central South University, Changsha 410017, China.

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|March 29, 2023
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Summary

This study introduces a defense scheme to protect continuous-variable quantum key distribution (CVQKD) systems from adversarial attacks targeting neural networks. The proposed method enhances CVQKD security by effectively filtering disturbances and improving monitoring capabilities.

Keywords:
CVQKDadversarial attacklocality preserving projectssparse defense

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Area of Science:

  • Quantum Information Science
  • Cybersecurity
  • Machine Learning Applications

Background:

  • Continuous-variable quantum key distribution (CVQKD) systems face practical attack strategies.
  • Neural networks, while useful for detecting these attacks, are susceptible to adversarial attacks, posing security risks to CVQKD.
  • Existing defense mechanisms may not fully address the nuances of adversarial disturbances in CVQKD.

Purpose of the Study:

  • To propose and evaluate a novel defense scheme to enhance the security of CVQKD systems against adversarial attacks.
  • To mitigate the vulnerabilities introduced by neural network applications in CVQKD security.
  • To improve the robustness and reliability of CVQKD systems in the presence of sophisticated threats.

Main Methods:

  • Employing regularized self-representation-locality preserving projects (RSR-LPP) for low-rank dimensionality reduction to filter initial adversarial disturbances.
  • Utilizing dictionary learning and sparse coding reconstruction to refine data details and eliminate residual adversarial disturbances.
  • Testing the proposed defense algorithm within a practical CVQKD system environment.

Main Results:

  • The proposed defense scheme demonstrates a significant monitoring and alarm capability against adversarial disturbances in CVQKD.
  • The RSR-LPP and sparse coding approach effectively filters both initial and residual adversarial perturbations.
  • Comparative analysis shows superior performance of the proposed scheme over existing defense algorithms.

Conclusions:

  • The developed defense scheme offers a robust solution for securing CVQKD systems against neural network-based adversarial attacks.
  • The combination of dimensionality reduction and sparse coding proves effective in enhancing CVQKD system resilience.
  • This work contributes to the practical security and trustworthiness of quantum communication technologies.