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This study introduces a novel framework using blockchain and unlearnable examples (UEs) to protect sensitive data used in large language model (LLM) training. The approach enhances data privacy against unauthorized misuse and reverse attacks.

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Area of Science:

  • Artificial Intelligence
  • Cybersecurity
  • Data Science

Background:

  • Large language models (LLMs) utilize contrastive learning on vast datasets, raising data privacy concerns.
  • Unlearnable Examples (UEs) protect sensitive data by disrupting model training but face challenges like perturbation reversal and data traceability.

Purpose of the Study:

  • To propose a Blockchain-Integrated Unlearnable Example Generation and Management Framework (B-UEGMF) for robust data privacy.
  • To address the limitations of existing UE generation methods regarding reversibility and data management.

Main Methods:

  • Developed a Blockchain-Integrated Unlearnable Example Generation and Management Framework (B-UEGMF).
  • Utilized blockchain for immutable storage of example hash values and smart contracts for dynamic access control.
  • Generated UEs using Dynamic Error-Minimizing Noise (DEM), a multi-objective perturbation technique.

Main Results:

  • The B-UEGMF framework demonstrated enhanced robustness against perturbation reversal attacks.
  • Quantitative evaluations confirmed improved privacy protection capabilities of the generated UEs.
  • The framework ensures efficient data privacy management and traceability.

Conclusions:

  • The proposed B-UEGMF effectively safeguards sensitive data against unauthorized access and misuse in LLM training.
  • Blockchain integration provides a secure and transparent mechanism for managing unlearnable examples.
  • The DEM technique significantly improves the resilience of UEs against sophisticated reversal methods.