深度学习是基于建议的混合区块链模型的个性化构建方法.
Xiaomo Yu1,2, Wenjing Li3,4, Xiaomeng Zhou3
1Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, Guangxi, China. yuxiaomo@nnnu.edu.cn.
Scientific reports
|October 20, 2023
概括
本研究介绍了一种使用深度学习和混合区块链模型的新型个性化推系统 (PRS). 新系统提高了安全性和效率,实现了较低的延迟和预测错误.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 个性化推系统 (PRS) 面临着信息泄露等安全挑战.
- 深度学习模型提供了改善PRS性能的潜力.
- 区块链技术可以提高分布式系统中的数据安全性和完整性.
研究的目的:
- 使用混合区块链模型开发一个安全高效的个性化推系统 (PRS).
- 提高基于深度学习的PRS的性能和安全性.
- 解决现有PRS中的信息泄露问题.
主要方法:
- 构建一个个性化推系统 (PRS),将深度学习与混合区块链模型集成在一起.
- 设计用于公共链的杆-拜占庭-阿尔戈兰-定向环形图 (PBDAG) 协商一致算法的授权证明算法.
- 开发一个个性化的推模型,将PBDAG共识算法与优化的反向传播算法结合起来.
主要成果:
- 与传统的共识算法相比,提出的模型显示了较低的平均延迟时间.
- 实现了80%的稳定数据消息传递率.
- 数据消息泄露率稳定在约10%.
- 系统分类预测错误仍然低于10%.
结论:
- 开发的基于区块链的混合PRS提供了增强的网络安全性和低延迟性能.
- 该模型促进了更有效,更准确的信息交互.
- 这项研究为数据驱动的PRS在各个领域的信息安全提供了实验基础.
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