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针对目前大数据环境中存在的数据安全问题，提出一种基于Hadoop大数据平台和无简并高维离散超混沌系统的加密算法.算法采用流密码对称加密方式，在Hadoop平台上读取存储于HDFS（Hadoop distributed file system）的大数据，进行分片处理和MapReduce编程后，用Map函数实现数据并行加密和解密，通过Reduce函数实现数据的合并操作并存储于HDFS.该算法具有较好的执行效率.与正李氏指数发生简并的低维混沌系统相比，无简并高维离散超混沌加密算法能提高系统安全性能，李氏指数均为正并且足够大，具有更好的统计特性，可通过严格的TESTU01测试，并行加密的密文之间互相关性很小.密钥参数众多使得估计或辨识难度增大.在密文闭环反馈条件下，具有抵御已知明文攻击和选择明文攻击的能力.Aiming at the data security problem in big data environment, in this paper we propose a new chaotic encryption algorithm based on both big data platform named Hadoop and non-degenerate high-dimensional discrete hyperchaotic system. The algorithm utilizes the chaotic stream cryptography and reads the data from HDFS of Hadoop platform. After fragmentation processing and MapReduce programming, the data are encrypted and decrypted by Map function in parallel. The Reduce function implements the merging operation of the data and stores them on the HDFS. The algorithm has a better execution efficiency. Compared with the low-dimensional chaotic system based encryption algorithm, the non-degenerate high-dimensional discrete chaotic system based encryption algorithm can improve the system security performance. It can pass the strict TESTU01 test with better statistical properties and make sure that the correlation with the parallel ciphertext is very small. Numerous key parameters increase the difficulty in making estimation or identification. Under the closed-loop feedback in ciphertext, it has the ability to resist the known and chosen plaintext attacks.
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Keywords:
- chaotic system /
- chaotic cipher /
- Hadoop
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[22] Termonia Y 1984 Phys. Rev. A 29 1612
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[1] Mayer-Schnberger V, Kenneth C 2013 Big Data:A Revolution That Will Transform How We Live, Work and Think (London:John Murray) pp1-15
[2] Feng D G, Zhang M, Li H (in Chinese)[冯登国,张敏,李昊 2014 计算机学报 37 246]
[3] Meng S, Dou W, Zhang X 2014 IEEE Trans. Parall. Distr. 25 3221
[4] Wang J H, Liu C Y, Fang B X (in Chinese)[王佳慧, 刘川意, 方滨兴 2016 通信学报 37 142]
[5] Yang C, Lin W, Liu M 2013 IEEE International Conference on Emerging Intelligent Data and Web Technologies Xi'an, China, September 9-11, 2013 p437
[6] Yu Q, Ling J (in Chinese)[余琦, 凌捷 2013 计算机工程与设计 34 2700]
[7] Li M, Yang C, Tian J 2015 IEEE International Conference on Computational Intelligence & Communication Technology Ghaziabad, India, February 13-14, 2015 p4799
[8] Shetty M M, Manjaiah D H 2016 IEEE International Conference on Emerging Technological Trends Kollam, India, October 21-22, 2016 p5090
[9] Han D, Min L, Chen G 2016 Int. J. Bifurcat. Chaos 26 1650091
[10] Liu H, Wang X, Kadir A 2014 Int. J. Nonlin. Sci. Num. 15 1565
[11] Wang C F, Ding Q 2017 Acta Phys. Sin. 66 020504 (in Chinese)[王传福, 丁群 2017 66 020504]
[12] Lin Z S, Yu S M, L J H 2015 IEEE Trans. Circ. Syst. Vid. 25 1203
[13] Mirzaei O, Yaghoobi M, Irani H 2012 Nonlinear Dyn. 67 557
[14] Zhou Q, Wong K W, Liao X 2008 Chaos Soliton Fract. 38 1081
[15] Wang X Y, Yang G, Min Z E (in Chinese)[王欣宇, 杨庚, 闵兆娥 2015 计算机应用研究 32 1757]
[16] Si H W, Zhong G Y (in Chinese)[司红伟, 钟国韵 2015 计算机测量与控制 23 2475]
[17] Chen Z, Yuan X, Yuan Y 2016 IEEE Trans. Circuits I 63 1464
[18] Ho W H, Chou J H, Guo C Y 2010 Nonlinear Dyn. 61 29
[19] Sun J, Zhao J, Wu X, Fang W, Cai Y, Xu W 2010 Phys. Lett. A 374 2816
[20] Chang J F, Yang Y S, Liao T L, Yan J J 2008 Expert Syst. Appl. 35 2074
[21] Zhao L, Liao X F, Xiang T, Xiao D 2010 Acta Phys. Sin. 59 1507 (in Chinese)[赵亮, 廖晓峰, 向涛, 肖迪 2010 59 1507]
[22] Termonia Y 1984 Phys. Rev. A 29 1612
[23] Wang F, Zhang X Z, Shen C W, Yu S M 2012 Acta Phys. Sin. 61 190505 (in Chinese)[王芳, 张新政, 申朝文, 禹思敏 2012 61 190505]
[24] White T (Zeng D D, Transl.) 2015 Hadoop:The Definitive Guide (Beijing:Tsinghua University Press) pp80-82 (in Chinese)[怀特 (曾大聃, 译) 2015 Hadoop权威指南 (北京:清华大学出版社) 第80–82页]
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