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Related Experiment Video

Updated: Jun 7, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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Countering adversarial perturbations in graphs using error correcting codes.

Saif Eddin Jabari1

  • 1<a href="https://ror.org/00e5k0821">New York University Abu Dhabi</a>, Saadiyat Island, P.O. Box 129188, Abu Dhabi, United Arab Emirates and <a href="https://ror.org/0190ak572">New York University Tandon School of Engineering</a>, Brooklyn, New York 11201, USA.

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|November 20, 2024
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Summary

This study introduces a novel graph defense strategy using repetition coding and majority voting to secure data transmission against adversarial attacks. The method effectively reconstructs graphs without needing prior knowledge of attack specifics.

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Area of Science:

  • Graph theory
  • Network security
  • Information theory

Background:

  • Graphs are vulnerable to adversarial perturbations during transmission, akin to cyber attacks.
  • Edge additions or removals can compromise graph integrity.
  • Existing defenses may require prior knowledge of attack vectors.

Purpose of the Study:

  • To develop a robust method for reconstructing graphs subjected to adversarial edge perturbations.
  • To propose a defense mechanism that operates without prior knowledge of attack characteristics.
  • To analyze the effectiveness of the proposed method on different graph models.

Main Methods:

  • Utilizing a repetition coding scheme with sender-assigned noise.
  • Implementing majority voting on the receiver's end for graph rectification.
  • Analytically deriving bounds on the number of repetitions for probabilistic constraints.

Main Results:

  • The method successfully decodes Erdős-Rényi graphs with non-random edge removal (highest eigenvector centrality) and random edge manipulation.
  • Effective against attacks on scale-free graphs (Barabási-Albert model).
  • Requires more repetitions for scale-free graphs compared to Erdős-Rényi graphs.

Conclusions:

  • The proposed repetition coding and majority voting scheme offers a robust solution for graph reconstruction against adversarial perturbations.
  • The method demonstrates effectiveness across different graph types and attack strategies.
  • Analytical bounds provide a quantitative measure for ensuring reconstructed graph quality.