基于PKI技术的用户身份数据转发认证算法仿真

Na-rengerile LIU, Hao-riqin WANG

Computer Simulation（2020）

Cited 3|Views5

Abstract

针对传统认证算法容易将属性安全数据当成危险数据进行隔离,影响用户数据处理效率的问题,提出PKI技术的用户身份数据转发认证算法.域间安全认证根据用户服务双方信息来源进行信任计算,实现对自身信任的初始化处理,将用户直接信息与间接信息进行结合,根据结合结果完成域间用户身份数据转发安全认证算法的构建;对于域内认证,通过虚拟组织进行安全认证,并按照用户身份数据转发安全要求选择相应的认证方式.在域间认证采用身份的PKI技术,通过身份的密钥交换协议设计了一种安全认证算法.实验结果表明,所提算法进行安全认证下用户身份数据相关性强,错误认证情况最少;通过所提算法构建的算法有很高的安全认证可靠性,鲁棒性较高.

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper