Mining association rules between sets of items in large databases
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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Privacy Preserving Data Mining
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EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
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ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
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CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
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Data & Knowledge Engineering
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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 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.