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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Private Mining of Association Rules
verfasst von : Justin Zhan, Stan Matwin, LiWu Chang
Erschienen in: Intelligence and Security Informatics
Verlag: Springer Berlin Heidelberg
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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.