Privacy-preserving collaborative association rule mining

Authors:
Justin Zhan;Stan Matwin;LiWu Chang
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Canada;School of Information Technology and Engineering, University of Ottawa, Canada;Center for High Assurance Computer Systems, Naval Research Laboratory, USA
Venue:
Journal of Network and Computer Applications
Year:
2007

Citing 8
Cited 8

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An efficient cacheable secure scalar product protocol for privacy-preserving data mining

DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Implementation, optimization and performance tests of privacy preserving mechanisms in homogeneous collaborative association rules mining

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ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
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AISC '11 Proceedings of the Ninth Australasian Information Security Conference - Volume 116

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Abstract

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.