Privacy-Preserving Collaborative Association Rule Mining

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 parties involved in a data mining task. We study how to share private or confidential data in the following scenario: multiple 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 multiple 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. Key Words: Privacy, security, association rule mining.

• Type: Conference
• Year: 2004
• Where: ICEB
• Authors: Justin Z. Zhan, Stan Matwin, Nathalie Japkowicz, LiWu Chang
• Keywords: Association Rule Mining, Business, Data Sharing, ICEB 2004, Multiple Parties