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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Zhan, J., Matwin, S., & Chang, L. W. (2005). Private mining of association rules. In Lecture Notes in Computer Science (Vol. 3495, pp. 72–80). Springer Verlag. https://doi.org/10.1007/11427995_7
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