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.
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Aggarwal, G., Mishra, N., Pinkas, B.: Secure computation of the k th-ranked element. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 40–55. Springer, Heidelberg (2004)
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)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the ACM SIGMOD Conference on Management of Data, May 2000, pp. 439–450. ACM Press, New York (2000)
Dwork, C., Nissim, K.: Privacy-preserving datamining on vertically partitioned databases
Goldreich, O.: Secure multi-party computation, working draft (1998), http://www.wisdom.weizmann.ac.il/home/oded/public_html/foc.html
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)
Kantarcioglu, M., Clifton, C.: Privacy preserving data mining of association rules on horizontally partitioned data. In: Transactions on Knowledge and Data Engineering. IEEE Computer Society Press, Los Alamitos (to appear)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, p. 36. Springer, Heidelberg (2000)
Luby, M. (ed.): Pseudorandomness and Cryptographic Applications. Princeton University Press, Princeton (1996)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570
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)
Yao, A.C.: Protocols for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science (1982)
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Zhan, J., Matwin, S., Chang, L. (2005). Privacy-Preserving Collaborative Association Rule Mining. In: Jajodia, S., Wijesekera, D. (eds) Data and Applications Security XIX. DBSec 2005. Lecture Notes in Computer Science, vol 3654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11535706_12
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DOI: https://doi.org/10.1007/11535706_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28138-2
Online ISBN: 978-3-540-31937-5
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