Abstract
Privacy concerns have become an important issue in Data Mining. This paper deal with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each individual database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each. We present two algorithms for discovering frequent item sets and analyze their security, privacy and complexity properties.
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R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, Sept. 12–15 1994.
A. Prodomidis, P. Chan, S. Stofo. Meta-learning in distributed data mining systems: Issues and approaches, chapter 3. AAAI/MIT Press, 2000.
J. Vaidya, C. Clifton. Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In Proceedings of SIGKDD 2002, Edmonton, Alberta, Canada.
A.C. Yao. How to generate and exchange secrets. In Proceedings of the 27th IEEE Symposium on Foundations of Computer Science, 1986, pages 162–167.
O. Goldreich, S. Micali, A. Wigderson. How to play any mental game-a completeness theorem for protocols with honest majority. In Proceedings, 19th ACM Symposium on the Theory of Computing, pages 218–229, 1987.
W. Du and M.J. Atallah,. Secure multi-party computational geometry. In proceedings of the 7th International Workshop on Algorithms and Data Structures, Providance, Rhode Island, 2001.
R. Agrawal, T. Imielinski, A.M. Swami. Mining association rules between sets of items in large databases., In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington, 1993.
G. Brassard, C. Crepeau, J. Robert. All-or-nothing disclosure of secrets. In Advances in Cryptology-Crypto86, Springer Lecture Notes in Computer Science, volume 263, 1986.
I. Even, O. Goldreich, A. Lempel. A randomized protocol for signing contracts. Communications of the ACM, Vol 28:637–647, 1985.
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Gudes, E., Rozenberg, B. (2004). Collaborative Privacy Preserving Frequent Item Set Mining in Vertically Partitioned Databases. In: De Capitani di Vimercati, S., Ray, I., Ray, I. (eds) Data and Applications Security XVII. IFIP International Federation for Information Processing, vol 142. Springer, Boston, MA. https://doi.org/10.1007/1-4020-8070-0_7
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DOI: https://doi.org/10.1007/1-4020-8070-0_7
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