Improved scheme of content extraction signatures based on RSA

Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (24): 96-99.

Previous Articles Next Articles

Improved scheme of content extraction signatures based on RSA

LI Xu1, DU Xiaoni2, WANG Caifen1, ZHENG Yahong1, ZHANG Ji1

1.College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
2.College of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, China

Online:2014-12-15 Published:2014-12-12

基于RSA的可截取签名改进方案

李旭1，杜小妮2，王彩芬1，郑亚红1，张记1

1.西北师范大学计算机科学与工程学院，兰州 730070
2.西北师范大学数学与统计学院，兰州 730070

Abstract

Abstract: Content Extraction Signatures（CES） improve the efficiency of multiparty interaction and resolve the problem of multiple signature and communication. Documents’ format becomes messy after messages extraction in the traditional CES scheme. To solve this problem, based on RSA, combining the batch signature idea, this paper promotes a new CES scheme and analyses the effects of the extractor’s behaviors to the verification of signature. Compared with the traditional CES, the new scheme optimizes the structure of the extracted document and improves the signature efficiency without reducing the security performance.

Key words: RSA, Content Extraction Signatures（CES）, batch signatures, context extraction access structure, chosen-plaintext attack

摘要： 在多方参与的签名环境中，可截取签名体制（CES）解决了信息的多次签名和多次传递等问题。对现有CES方案进行了分析，发现现有方案截取后的文档存在版式凌乱的缺陷。针对该缺陷，结合批签名思想，提出了一种基于RSA的可截取签名改进方案，并分析了截取者的各种行为对签名验证的影响?。研究表明，新方案在保持原方案安全性能不变的基础上，优化了截取文档的结构，提高了签名效率。

关键词: RSA, 可截取签名, 批签名, 截取访问结构, 选择明文攻击

LI Xu1, DU Xiaoni2, WANG Caifen1, ZHENG Yahong1, ZHANG Ji1. Improved scheme of content extraction signatures based on RSA[J]. Computer Engineering and Applications, 2014, 50(24): 96-99.

李旭1，杜小妮2，王彩芬1，郑亚红1，张记1. 基于RSA的可截取签名改进方案[J]. 计算机工程与应用, 2014, 50(24): 96-99.

[1]	ZHANG Bo, XU Liming, HUANG Zhiwei, YAO Xiaopeng. Multi-objective GANs Pareto Optimality Algorithm Using Gradient Strategy [J]. Computer Engineering and Applications, 2021, 57(9): 89-95.
[2]	CHAI Xu, FANG Ming, FU Feiran, SHAO Zhen. Sight Estimation Algorithms for Examinee in Examination Room Environment [J]. Computer Engineering and Applications, 2021, 57(9): 199-206.
[3]	WU Wenlong, ZHOU Xi, WANG Yi, WANG Baoquan. WKAG：Fraud Detection Method for Imbalanced Medical Insurance Data [J]. Computer Engineering and Applications, 2021, 57(9): 247-254.
[4]	WANG Jinyu, YANG Haitao, LI Gaoyuan, ZHANG Changgong, FENG Bodi. Research Progress of Generative Adversarial Network and Its Application in Image Processing [J]. Computer Engineering and Applications, 2021, 57(8): 26-35.
[5]	LI Jian, SUN Dasong, ZHANG Beiwei. Image Restoration Using Dual-Encoder and Adversarial Training [J]. Computer Engineering and Applications, 2021, 57(7): 192-197.
[6]	ZHAI Zhengli, LI Penghui, FENG Shu. Research Overview of Adversarial Attacks on Graphs [J]. Computer Engineering and Applications, 2021, 57(7): 14-21.
[7]	WAN Mengxiang, YAO Hanbing. GAN Model for Malicious Web Training Data Generation [J]. Computer Engineering and Applications, 2021, 57(6): 124-130.
[8]	ZHANG Rui, WU Boxiong, ZHANG Liyuan, ZHANG Bo. Human Trajectory Prediction Method for Complex Scenes [J]. Computer Engineering and Applications, 2021, 57(6): 138-143.
[9]	XU Xiaochun, DONG Hongwei, WEI Chengfeng. Application of Improved CAGAN in Virtual Try-on [J]. Computer Engineering and Applications, 2021, 57(6): 152-158.
[10]	ZOU Chengming, HU Youpu. Monocular Depth Estimation in Outdoor Scene with Generative Adversarial Network [J]. Computer Engineering and Applications, 2021, 57(6): 176-183.
[11]	CHEN Renhe, LAI Zhenyi, QIAN Yurong. Improved Image Denoising Generative Adversarial Network Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 168-172.
[12]	ZHOU Huaqiang, CAO Lin, DU Kangning. Sketch Face Synthesis Based on Multi-discriminator Cyclic Generative Adversarial Network [J]. Computer Engineering and Applications, 2021, 57(3): 231-238.
[13]	XIA Hao, LYU Hongfeng, LUO Jun, CAI Nian. Survey on Deep Learning Based Image Super-Resolution [J]. Computer Engineering and Applications, 2021, 57(24): 51-60.
[14]	MA Zhihao, ZHU Xiangbin. Research on Quasi-hyperbolic Momentum Gradient for Adversarial Deep Reinforcement Learning [J]. Computer Engineering and Applications, 2021, 57(24): 90-99.
[15]	YANG Luyue, ZHANG Shumei, ZHAO Junli. Dynamic Expression Recognition with Partial Occlusion Based on Parallel Gan [J]. Computer Engineering and Applications, 2021, 57(24): 168-178.

Improved scheme of content extraction signatures based on RSA

基于RSA的可截取签名改进方案

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics