Protocol Design for Privacy-Preserving Data Mining Using Partial Homomorphic Encryption

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 efficient 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.