Private Mining of Association Rules
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.
KeywordsPrivacy security association rule mining
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- 2.Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of ACM SIGMOD Conference on Management of Data, Washington D.C, May 1993, pp. 207–216 (1993)Google Scholar
- 4.Du, W., Zhan, Z.: Building decision tree classifier on private data. In: Workshop on Privacy, Security, and Data Mining at The 2002 IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, December 9 (2002)Google Scholar
- 5.Dwork, C., Nissim, K.: Privacy-preserving datamining on vertically partitioned databasesGoogle Scholar
- 6.Freedman, M., Nissim, K., Pinkas, B.: Effiicent private matching and set intersection. In: EUROCRYPT, pp. 1–19 (2004)Google Scholar
- 7.Goldreich, O.: Secure multi-party computation (working draft) (1998), http://www.wisdom.weizmann.ac.il/home/oded/public_html/foc.html
- 8.Vaidya, J., Clifton, C.W.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 23-26 (2002)Google Scholar
- 11.Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570Google Scholar
- 12.Wright, R., Yang, Z.: Privacy-preserving bayesian network structure computation on distributed heterogeneous data. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD (2004)Google Scholar
- 13.Yao, A.C.: Protocols for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science (1982)Google Scholar