本文提出三種強化同時壓縮與加密方法的技巧。這三種技巧分別改善現有方法之執行時間、壓縮效能及估計的準確度。第一種技巧利用輔助的資料結構顯著加速現有的基於混沌的同時壓縮與加密方法。第二種技巧藉由選取少數重要的子表格，解決使用多維表格時表格過大而影響壓縮效能的問題。第三種技巧以時間對資料流中之元素加權，藉以提高加密時使用之機率分佈估計模型的準確度。本文並提出以上述三種技巧為核心概念的兩種同時加密與壓縮的方法，分別應用在壓縮檔案及資料流上。實驗證明此兩種方法的壓縮效能以及執行時間都優於現有方法，也同時證實本文提出的三種技巧之實用性。

Three techniques for enhancing joint compression and encryption (JCAE) schemes are proposed. They respectively improve the execution time, compression performance and estimation accuracy of three different JCAE schemes. The first uses auxiliary data structures to significantly accelerate currently existing chaos-based join compression and encryption scheme. The second one solves the problem of huge multidimensional lookup table overheads by selecting a small number of important sub tables. The third increases the accuracy of frequency distribution estimations used for compressing streaming data by weighting symbols in the plaintext stream according to their position in the stream. Two joint compression and encryption schemes leveraging the above three techniques, one for static files and the other for streaming data, are proposed. Experiments results show that the proposed schemes run faster and generate smaller files than existing schemes, verifying that the three techniques are useful and practical.

口試委員審定書 i
誌謝 ii
摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES ix
Chapter 1 Introduction 1
1.1 Thesis organization 3
Chapter 2 Related Work 4
2.1 Look-up table and chaotic-map based approach 4
2.2 Dynamic updating look-up-table based approach 5
2.3 Number of distinct plaintext symbol based approach 5
2.4 Bi-gram based approach 5
2.5 Streaming approach 6
Chapter 3 Proposed Methods 7
3.1 A queue based non-relabeling scheme for lookup table updating 7
3.1.1 The timestamp table 8
3.1.2 The MPNS queue 8
3.1.3 The proposed scheme 8
3.1.4 Time Complexity analysis 9
3.2 A hybrid unigram and bigram context based lookup table selection mechanism 9
3.2.1 Hybrid bigram and unigram contexts 11
3.2.2 Bigram selection 11
3.3 An adaptive probability estimation modelling for streaming data 12
3.3.1 Retaining old information 13
3.3.2 Implementation 13
3.4 A static scheme 14
3.4.1 LUT creation 15
3.4.2 Chaos-based encryption 16
3.4.3 Huffman coding 16
3.4.4 Masking 17
3.5 A streaming scheme 17
3.5.1 Real-number LUTs’ issue 18
3.5.2 Context modelling 19
3.5.3 Bigram context selecting 20
3.5.4 The proposed procedure 21
Chapter 4 Performance and Security Analyses 22
4.1 Compression Ratio 22
4.2 Execution Time 23
4.3 Security Analysis 24
4.3.1 Key space 24
4.3.2 Key sensitivity 24
4.3.3 Plaintext sensitivity 25
Chapter 5 Conclusion 27
REFERENCE 28

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