一种有效的隐私保护区块链存储方法,用于物联网环境环境
Dayu Jia1, Guanghong Yang1,2, Min Huang1,2
1College of Information Science and Engineering, Northeastern University, 110819 Shenyang, China.
概括
本研究介绍了一种保护隐私的区块链存储方法,用于物联网 (IoT). 它使用联合学习来保护用户数据,同时在分片区块链中提高数据查询速度和效率.
科学领域:
- 计算机科学 计算机科学
- 信息技术 信息技术 信息技术
- 网络安全 网络安全
背景情况:
- 分散的信任管理至关重要,基于分片的区块链被提议用于资源有限的物联网 (IoT).
- 机器学习通过分类和本地存储热数据来提高分片区块链中的查询效率.
- 现有的方法面临隐私挑战,因为用于学习的区块功能暴露了.
研究的目的:
- 为物联网环境提出一种高效且保护隐私的区块链存储方法.
- 为解决与用于分片区块链的机器学习模型中使用区块功能相关的隐私问题.
- 为了提高数据查询速度,并在物联网区块链应用中保护用户隐私.
主要方法:
- 一种新的保护隐私的区块链存储方法,利用联合极端学习机器进行热块分类.
- 拟议方法与ElasticChain集成,这是一个分片区块链模型,用于高效的热块存储.
- 定义五个全面的特征来评估热块:客观特征,历史流行,潜在流行,存储要求和培训价值.
主要成果:
- 拟议的方法通过防止其他节点访问热块功能来有效地保护用户隐私.
- 热块的本地存储可以在物联网环境中显著提高数据查询速度.
- 合成数据的实验结果验证了拟议的区块链存储模型的准确性和效率.
结论:
- 开发的方法为物联网中的保护隐私的区块链存储提供了有效的解决方案.
- 联合学习是一种可行的方法,可以在分片区块链系统中增强安全性和隐私.
- 该模型在物联网数据管理的准确性,效率和隐私保护方面表现出强的表现.
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