安全多方計算是資訊安全研究裡的一個重要主題，其概念為多方在不洩漏各自私有資訊下能一起完成某種函式的計算。在安全多方計算研究領域裡，有一種作法是以scalar product來當作計算的基礎演算邏輯單元，重而建構其他更複雜的安全多方計算。本論文首先針對scalar product發展一套平行性實作架構，藉此我們再實作出多個不同演算法之comparison計算，其中包含了循序演算法以及平行演算法。我們透過實驗來找出適當的平行計算基礎架構與影響執行時間效能的主要因子，並以執行時間效能上的分析來推導相關時間公式。由上述實證研究我們對於不同演算法之comparison計算來作執行時間效能的預測，從實驗結果可以得知我們推導出來之時間公式極為準確，希望能給予使用者在執行comparison計算有所考量，使其在不同執行環境執行comparison計算能有最佳的執行時間效能。 / Loosely speaking, secure multi-party computation (SMC) involves computing functions with inputs from two or more parties in a distributed network while ensuring that no additional information, other than what can be inferred from each participant’s input and output, is revealed to parties not privy to that information. This thesis concerns the parallel implementation of SMC using a scalar-product (SP) based approach. In this approach, SP is considered as the basic building block for constructing more complex SMC. My thesis first develops a concurrent architecture for implementing two-party scalar product computation. Then it implements several algorithms of secure comparison. Finally, a series of experiments are conducted to collect performance statistics for building time functions that can predict the execution time of comparison computation based on that of the scalar product and other parameters, such as CPU core numbers. From the experimental results, we find that these time functions are very accurate. Hence we argue that these time functions can assist users to obtain the better runtime performance for comparison protocols under their specific execution environments.

本論文對線性三對角方程組之解法提出平行演算法於超立方體網路 ( hypercube network), 並且此平行演算法能達到最佳費用 (optimal cost ) O(N). 討論的解法包含 (1)循環消減法 (cyclic reduction method)及 (2)高斯消去法 (Gaussian elimination method), 基於 (1)法之平行演算法當使用處理器個數為 O(N/logN)時, 其執行時間為 O( logN); 基於 (2) 法之平行演算法當使用處理器個數為 O(N/(logN)^2) 時, 其執行時間為 O((logN)^2); 費用 (cost) 等於處理器個數乘以執行 時間.