在混合区块链上实现可验证的决策树预测
Moxuan Fu1, Chuan Zhang1,2, Chenfei Hu1
1School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
这项研究介绍了VDT,这是一个用于区块链上可验证的决策树预测的新方案. 它确保了云服务中机器学习模型输出的完整性,提供了高效的验证证明.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 机器学习,特别是决策树,在云服务中被广泛使用.
- 确保在线决策树预测的完整性和正确性是一个重大挑战.
- 区块链技术提供了适合可验证机器学习的不可变和可追溯的功能.
研究的目的:
- 为区块链系统上的决策树预测服务提出一个安全和可验证的方案.
- 为了解决在线决策树预测中日益关注的完整性问题.
- 使客户能够验证服务提供商对模型预测的正确性.
主要方法:
- 利用默克尔树和哈希函数来创建决策树预测的验证证明.
- 开发可验证的决策树 (VDT) 预测方案.
- 将该计划扩展到可验证决策树的高效更新方法.
主要成果:
- 拟议的VDT方案使服务提供商能够为决策树预测生成验证证据.
- 该方案允许对可验证的决策树模型进行高效更新.
- 安全证明证明了VDT方案的稳定性.
- 实验评估表明,证明生成时间不到1秒.
结论:
- 该VDT方案有效地提高了云环境中决策树预测的安全性和可验证性.
- 区块链集成为安全的机器学习服务提供了可靠的框架.
- 提出的方法提供了一个实际的解决方案,以确保对在线机器学习预测的信任.
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