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基于盲源分离的语音数据加密算法

作　者: Amna Saga Mohammed Humidan
导　师: 韩太林
学　校: 长春理工大学
专　业: 信息与通信工程
关键词: Speech Encryption Blind Source Separation Independent ComponentAnalysis Kronecker product Matrix Convolution
分类号: TN918.4
类　型: 硕士论文
年　份: 2013年
下　载: 1次
引　用: 0次
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内容摘要

The huge revolution of internet and information technology brought with it a high demand to develop new cryptosystems that provide adequate data security. For speech transmition, there are many cryptosystems exist, which are classified either as analog encryption or digital encryption (sometimes is called scrambling) such as chaos-based encryption.This thesis proposed a new speech encryption algorithm based on Blind Source Separation (BSS). Blind source separation is statistical approach used to separate independent source signals from their mixture without prior knowledge to the mixing coefficients. The basic idea of the proposed algorithm is to utilize the difficulty of solving the underdetermined BSS problem where the number of source signals is greater than the observations. First step and According to BSS mathematical principles, inseparable underdetermined mixing matrix was formed. Second, the mixing matrix then used to encrypt the segmented speech signals by mixing it with key signals which are pseudorandomly generated by Pseudo Random Number Generator (PRNG) and has the same number and size of the speech signals. To form the mixing matrix, Kronecker product of matrices and matrix convolution were used to construct two different forms of mixing matrix with high sensitivity to initial value changes. This important property of the mixing matrix increases the sensitivity of the encryption key thus increases the encryption algorithm security against plaintext attack, chosen plaintext attack, chosen ciphertext attack and other modern attacks. Third, to recover the speech signals back, Blind source separation by Fast lndependent Component Analysis "FastICA" algorithm vere used to separate the speech signals from the key signals. Finally, to realize this work, simulate experiments using Matlab R2009a software was done to simulate the algorithm then the results were analyzed and the efficiency and quality of the proposed speech cryptosystem is proved.

全文目录

Abstract  4-8
Chapter one:Introduction  8-10
  1.1 Introduction  8-9
  1.2 Thesis's main contents  9
  1.3 Thesis overview  9-10
Chapter two:Blind Source Separation  10-18
  2.1 Historical background  10-11
  2.2 BSS problem  11-12
  2.3 BSS linear model  12-13
  2.4 Independent Component Analysis ICA  13-15
    2.4.1 Statistical Independence  13
    2.4.2 Non-Gaussianity  13-14
    2.4.3 Measuring of non-Gaussianity  14-15
      2.4.3.1 Kurtosis  14
      2.4.3.2 Negentropy  14-15
  2.5 FastICA algorithm  15-17
    2.5.1 FastICA for one Independent Component  15
    2.5.2 FastICA for several Independent Components  15-16
    2.5.3 Properties of FastICA algorithm  16-17
  2.6 Summary  17-18
Chapter three:Matrix Calculus  18-25
  3.1 Kronecker product  18-21
    3.1.1 Definition  18-19
    3.1.2 Kronecker product properties  19
    3.1.3 Generalized kronecker product  19-21
  3.2 Matrix convolution  21-24
    3.2.1 Definition  21
    3.2.2 Convolution proprieties  21
    3.2.3 Vector convolution  21-24
  3.3 Summary  24-25
Chapter four:Speech encryption based on Blind Source Separation  25-42
  4.1 The encryption process  26-29
    4.1.1 Segmentation  26-27
    4.1.2 Constructing the underdetermined mixing matrix  27-29
      4.1.2.1 Underdetermined mixing matrix structure 1  27-29
      4.1.2.2 Underdetermined mixing matrix structure 2  29
  4.2 The decryption process  29-31
    4.2.1 BSS separation  29-30
    4.2.2 Waveform reconstruction  30-31
      4.2.2.1 Sequence reorder  30-31
      4.2.2.2 Amplitude and phase correction  31
  4.3 Security analysis  31-32
    4.3.1 Key space  32
    4.3.2 Key sensitivity  32
    4.3.3 Inseparability of the mixing matrix  32
  4.4 Simulation  32-38
  4.5 Performance analysis  38-40
  4.6 Conclusion  40-42
Acknowledgement  42-43
Bibliography  43-45

基于盲源分离的语音数据加密算法

内容摘要

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