基于区块链的框架,用于生成和管理无法学习的例子,以加强数据隐私和访问控制
Ruijia Li1, Zijiao Zhang2, Shouli Fu1
1School of Cyberspace Security, Zhengzhou University.
Journal of visualized experiments : JoVE
|September 8, 2025
概括
本研究引入了使用区块链和不可学习的例子 (UE) 的新框架,以保护大型语言模型 (LLM) 培训中使用的敏感数据. 这种方法增强了数据隐私,防止未经授权的滥用和反向攻击.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 数据科学数据科学数据科学
背景情况:
- 大型语言模型 (LLM) 在庞大的数据集上使用对比学习,引发了数据隐私方面的担忧.
- 无法学习的例子 (UE) 通过破坏模型训练来保护敏感数据,但面临诸如扰乱逆转和数据可追溯性等挑战.
研究的目的:
- 为强大的数据隐私提出一个区块链集成的不可学习的示例生成和管理框架 (B-UEGMF).
- 解决现有的UE生成方法在可逆性和数据管理方面的局限性.
主要方法:
- 开发了一个区块链集成的不可学习的示例生成和管理框架 (B-UEGMF).
- 利用区块链进行不变的存储,例如哈希值和用于动态访问控制的智能合约.
- 使用动态误差最小化噪声 (DEM),一种多目标扰动技术生成的UE.
主要成果:
- B-UEGMF框架表现出针对扰乱逆转攻击的增强强性.
- 定量评估证实了生成的UE的改善隐私保护能力.
- 该框架确保有效的数据隐私管理和可追溯性.
结论:
- 拟议的B-UEGMF有效地保护敏感数据免受未经授权的访问和滥用在LLM培训中.
- 区块链集成提供了一个安全和透明的机制来管理无法学习的例子.
- DEM技术显著提高了UE对复杂的逆转方法的弹性.
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