Communication-efficient three-party protocols for authentication and key agreement

AbstractEncrypted key exchange (EKE) authentication approaches are very important for secure communicating over public networks. In order to solve the security weaknesses three-party EKE, Yeh et al. [H.T. Yeh, H.M. Sun, T. Hwang, Efficient three-party authentication and key agreement protocols resistant to password guessing attacks, Information Science and Engineering 19 (6) (2003) 1059–1070.] proposed two secure and efficient three-party EKE protocols. Based on the protocol developed by Yeh et al., two improved EKE protocols for authentication and key agreement are proposed in this study. The computational costs of the proposed protocols are the same as those of the protocols of Yeh et al. However, the numbers of messages in the communication are fewer than those of the protocols of Yeh et al. Furthermore, the round efficient versions of our proposed protocols are also described

A study on genetic clustering methods and their applications to vector quantization and image compression

傳統上對於分類問題，例如K-means 及其類似的演算法，使用者在分類之前必須提供所須的類別個數，不幸的，通常使用者並不知道資料中所含的類別個數，因此，分類變成是一種嘗試錯誤的方法，對於分類個數較多的資料或對於大量的資料更難猜測其所包含的類別個數，本論文提供了兩個基因群聚演算法來解決分類問題，第一個演算法適合球形的類別，第二個演算法適合非球形的類別，它們可以依據資料的差異性，自動的找出適合的類別個數。 向量量化已經知道是一種對於語音及影像編碼極為有效的方法，循序搜尋的量化器必須搜尋整個碼本，近來樹狀結構量化器已被提出來，但是幾乎都是二元樹，也就是以人工的方式強迫節點分成兩個不同的類別，我們依據基因群聚演算法提出了一般樹狀結構量化器，利用一個誤差門檻保證其編碼的品質，而且，利用Huffman編碼來達到一般樹狀結構量化器的最佳位元率，對於漸次的編碼量化器則給定一序列的誤差門檻，最後提供與其他兩種樹狀結構量化器的效能比較。另外在影像壓縮方面，本論文提出了平滑邊比對分類向量量化器，包括有分類向量量化器、可變區塊大小、平滑邊比對方法， 並且與基因群聚演算法結合，由實驗結果可知比傳統的邊比對方法有較高的壓縮品質與壓縮率，Lena 影像可以達到0.172 bpp 和 32.49 dB.。In solving clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, the clustering becomes a tedious trial-and-error work and the clustering result is often not very promising especially when the number of clusters is large and not easy to guess. In this paper, we propose two genetic algorithms for the clustering problem. The first algorithm is suitable for clustering the data with compact spherical clusters. The second algorithm is suitable for clustering the data whose clusters may not be of compact spherical shape. They can automatically cluster the data according to the similarities and automatically find the proper number of clusters. Vector quantization had been proved to be a useful method for speech and image coding. The full-search vector quantization suffers from spending much time searching the whole codebook sequentially. Recently, several tree-structured vector quantizers had been proposed. But almost all trees used are binary trees and hence the training samples contained in each node are forced to be divided into two clusters artificially. We present a general-tree-structured vector quantizer that is based on a genetic clustering algorithm. A distortion threshold is used to guarantee the quality of coding. Also, the Huffman coding is used to achieve the optimal bit rate after the general-tree-structured coder was constructed. Progressive coding can be accomplished by given a series of distortion thresholds. A comparison of the performance of this vector quantizer and the other two tree-structured vector quantizers is given. Moreover, the proposed smooth side-match classified vector quantizer (SSM-CVQ) is the combination of three techniques: classified vector quantization, variable block size segmentation and the smooth side-match method. As indicated by the experimental results, SSM-CVQ has the higher coding quality and the lower bit rate than other methods have. The Lena image can be coded by SSM-CVQ with 0.172 bpp and 32.49 dB.Cover Acknowledgments Abstract(in Chinese) Abstract(in English) Table of contents Chapter 1 Introduction 1-1 The motivation 1-2 The genetic algorithin 1-3 The clustering problem 1-4 The vector quantization problem 1-5 The image compression problem 1-6 The overview of the thesis Chapter 2 A Historical Review 2-1 A survey of the clustering methods 2-2 A survey of the vector quantization methods 2-3 A survey of the compression methods Chapter 3 A Genetic Clustering Algorithm for Data with Spherical-Shape Clusters 3-1 The single linkage algorithm 3-2 The genetic clustering algorithm 3-3 A heuristic method for searching a good clustering 3-4 Experiments Chapter 4 A Genetic Clustering Algorithm for Data with Non-Spherical-Shape Clusters 4-1 The genetic clustering clgorithm 4-2 A heurisic method for searching a good clustering 4-3 Experiments Chapter 5 The General-Tree-Structured Vector Quantizer Using the Genetic Clustering Algorithm and the Huffman Coding 5-1 The design of the general tree coder and the Huffman decoding tree 5-2 5-3 Experimental Chapter 6 Smooth sode-match classified vector quantizer with variable block size 6-1 The smooth side-match method with variable block size 6-2 The smooth side-match classified variable quantizer 6-3 Experiments Chapter 7 Conclusions and future research works VITA References Publication

Codebook Generation Using Partition and Agglomerative Clustering

In this paper, we present a codebook generation algorithm to produce a codebook with lower distortion. Our method combines a fast codebook generation algorithm (CGAUCD) with doubling technique and fast agglomerative clustering algorithm (FACA) to generate a codebook with less computing time and lower distortion. Instead of using FACA directly to divide training vectors into M clusters, our proposed method first generates qM clusters from these training vectors, where q>1 is an integer, and then applies FACA to merge these qM clusters into M cells. This is due to the computational complexity of CGAUCD with doubling technique is less than that of FACA. These M cluster centers are used as the initial codebook for CGAUCD. Using three real images as the training set, our method can reduce the MSE and computing time of FPNN+CGAUCD, which is the available best method to our knowledge, by 0.19 to 0.38 and 74.6% to 84.3%, respectively