Abstract
We address the privacy preserving association rule mining problem in a system with one data miner and multiple data providers, each holds one transaction. The literature has tacitly assumed that randomization is the only effective approach to preserve privacy in such circumstances. We challenge this assumption by introducing an algebraic techniques based scheme. Compared to previous approaches, our new scheme can identify association rules more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.
This work was supported in part by the National Science Foundation under Contracts 0081761, 0324988, 0329181, by the Defense Advanced Research Projects Agency under Contract F30602-99-1-0531, and by Texas A&M University under its Telecommunication and Information Task Force Program. Any opinions, findings, conclusions, and/or recommendations expressed in this material, either expressed or implied, are those of the authors and do not necessarily reflect the views of the sponsors listed above.
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Zhang, N., Wang, S., Zhao, W. (2004). A New Scheme on Privacy Preserving Association Rule Mining. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Knowledge Discovery in Databases: PKDD 2004. PKDD 2004. Lecture Notes in Computer Science(), vol 3202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30116-5_44
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DOI: https://doi.org/10.1007/978-3-540-30116-5_44
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