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In this paper, the authors' survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to re-viewing definitions and constructions for secure multiparty computation, they discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. The authors' also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacy-preserving data mining. Finally, they discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.
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