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Privacy Preserving Data Mining - Pinkas

Privacy Preserving data Mining Yehuda LindellDepartment of Computer ScienceWeizmann Institute of Pinkas STAR Lab, Intertrust Technologies4750 Patrick Henry DriveSanta Clara CA this paper we address the issue of Privacy Preserving data Mining . Specifically, we consider ascenario in which two parties owning confidential databases wish to run a data Mining algorithm onthe union of their databases, without revealing any unnecessary information. Our work is motivatedby the need to both protect privileged information and enable its use for research or other above problem is a specific example of secure multi-party computation and as such, can besolved using known generic protocols. However, data Mining algorithms are typically complex and,furthermore, the input usually consists of massive data sets. The generic protocols in such a case areof no practical use and therefore more efficient protocols are required. We focus on the problem ofdecision tree learning with the popular ID3 algorithm.

1 Introduction We consider a scenario where two parties having private databases wish to cooperate by computing a data mining algorithm on the union of their databases.

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