Sciweavers

Share
ICEB
2004

Privacy-Preserving Collaborative Association Rule Mining

9 years 2 months ago
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.
Justin Z. Zhan, Stan Matwin, Nathalie Japkowicz, L
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where ICEB
Authors Justin Z. Zhan, Stan Matwin, Nathalie Japkowicz, LiWu Chang
Comments (0)
books