應用部份保密技術於密文資料探勘之協定設計

Abstract

由於電腦計算能力的提升，資料探勘可以從大量的資料中萃取出有用的資訊。2012年，每天會有2.5x1018位元組的資料產生。因此，當在多個參與者執行分散式的資料探勘演算法時，資料安全便成了重要的議題。在這篇論文中，我們聚焦在分散式資料探勘的安全技術提出了兩個協定---multi-party association rule mining (MP-ARM)以及multi-party decision tree learning (MP-DTL)。這兩個協定除了使用部分同形加密技術來達成安全資料探勘演算法，而且比目前存在的方法更有效率。藉由加入第三個參與者，可在多人的分散式資料探勘演算法中，完成大規模的資料探勘演算法。

With the advance of computing power, data mining techniques can extract useful information from large amount of data. In 2012, 2.5 quintillion bytes of data (1 follow 18 zeroes) are created every day. Data privacy is of utmost concern for distributed data mining across multiple parties, which may be competitors. In this thesis, we focus on the privacy preserving techniques in distributed data mining algorithms. We propose two protocols|multi-party association rule mining (MP-ARM) and multi-party decision tree learning (MP-DTL). Both protocols use partial homomorphic encryption to perform secure data mining algorithms, which are more e?cient than the existing work. With the aid from the third participant, two or more parties can securely perform large-scale data mining algorithms without revealing any additional information to the cloud servers.