期刊文献+

基于CF(p^n)的CCA安全ElGamal加密体制

CCA Secure Extended ElGamal Encryption Scheme Over CF(p^n)
下载PDF
导出
摘要 基于CF(pn)域上的离散对数困难性问题,提出一种基于CF(pn)域的ElGamal加密算法,并在标准模型下证明该加密算法满足IND-CCA安全性要求。该算法的安全参数k的大小由构成CF(pn)域的素数p和多项式的阶n共同决定,改变了传统ElGamal算法安全性对大素数p的唯一依赖的现状。利用C语言实现了基于CF(pn)的ElGamal算法,而且通过异或和移位这类简单操作即可编程实现。通过与RSA、ElGamal、ECC、AES算法的效率对比,发现基于CF(2n)的ElGamal算法在执行效率方面比传统的ElGamal加密算法快1 000倍,比RSA快3倍,比ECC快2 000倍。 This subject investigates the discrete logarithm problem over finite field CF ( p n ) , proposes a ElGamal encryption scheme over finite field CF(pn) , and proves that proposed the scheme satisfies IND - CCA security without oracle model. Futhermore, the size of algorithm,s security parameter k is contingent on prime p and polymonial degree n , which define the finite field CF(pn). The method alters the status in which traditional ElGamal algorithm security only relies on size of big prime p. We not only utilize C laguage to implement ElGamal algorithm over finite field CF(2n) ,but also ElGamal algorithm over finite field CF(2n) could be imple-mented by simple operations such as xor and shifting in aspect of programing implement. By means of comparing efficiencies with other exist schemes, such as RSA, traditional ElGamal, ECC, and AES algorithm. Then, we found that ElGamal algorithm over finite field CF(2n) runs 1 000 times faster than traditional ElGamal algorithm, three times faster than RSA, and 2 000 times faster than ECC.
出处 《西华大学学报(自然科学版)》 CAS 2017年第1期12-16,共5页 Journal of Xihua University:Natural Science Edition
基金 国家自然科学基金项目(61402376 U1433130) 教育部春晖计划项目(Z2014045) 西华大学研究生创新基金项目(ycjj2015192)
关键词 CF(pn)有限域 IND-CCA 安全参数 ElGamal加密方案 效率 finite field CF(pn) IND - CCA secure parameter ElGamal encryption scheme efficiencies
  • 相关文献

参考文献1

二级参考文献44

  • 1Zaharia M, et al. Resilient distributed datasets: A fault- tolerant abstraction for in-memory cluster computing// Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation. San Jose, USA, 2012 : 2-2.
  • 2Low Y, Bickson D, Gonzalez J, et al. Distributed GraphLab: A framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment, 2012, 5(8): 716-727.
  • 3Graham-Rowe D, Goldston D, Doctorow C, et al. Big data: Science in the petabyte era. Nature, 2008, 455(7209): 8-9.
  • 4Ghazal A, Rabl T, Hu M, et al. BigBench: Towards an industry standard benchmark for big data analytics//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. New York, USA, 2013 : 1197-1208.
  • 5Huang S, Huang J, Dai J Q, et al. The HiBench benchmark suite : Characterization of the MapReduce-based data analysis //Proceedings of the ICDE Workshops on Information Software as Services. LongBeaeh, USA, 2010:41-51.
  • 6Pavlo A, Paulson E, Rasin A, eta]. A comparison of approaches to farge-scale data analysis//Proceedings of the2009 ACM SIGMOD International Conference on Management of Data. Providence, USA, 2009:165-178.
  • 7Coper B, Silberstein A, Tam E, et al. Benchmarking cloud serving systems with YCSB//Proceedings of the 1st ACM Symposium on Cloud Computing. Indianapolis, USA, 2010: 143-154.
  • 8Armstrong T G, Ponnekanti V, Borthakur D, Callaghan M. LinkBench: A database benchmark based on the Facebook social graph//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. New York, USA, 2013:1185-1196.
  • 9Ferdman M, Adileh A, Koeberber O, et al. Clearing the clouds: A study of emerging scale-out workloads on modern hardware. ACM SIGPLAN Notices, 2012, 47(4): 37-48.
  • 10Burby J, Atchison S. Actionable Web Analytics: Using Data to Make Smart Business Decisions. New York, USA: John Wiley& Sons, 2007.

共引文献33

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部