Private Mining of Association Rules

This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among two parties involved in a data mining task. We study how to share private or confidential data in the following scenario: two parties, each having a private data set, want to collaboratively conduct association rule mining without disclosing their private data to each other or any other parties. To tackle this demanding problem, we develop a secure protocol for two parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning the association rules. Key Words: Privacy, security, association rule m...

• Type: Conference
• Year: 2005
• Where: ISI
• Authors: Justin Z. Zhan, Stan Matwin, LiWu Chang
• Keywords: Association Rule Mining, Data Sharing, ISI 2005, Private Data