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Enhancing the Efficiency in Privacy Preserving Learning of Decision Trees in Partitioned Databases

Lory, Peter


This paper considers a scenario where two parties having private databases wish to cooperate by computing a data mining algorithm on the union of their databases without revealing any unnecessary information. In particular, they want to apply the decision tree learning algorithm ID3 in a privacy preserving manner. Lindell and Pinkas (2002) have presented a protocol for this purpose, which enjoys ...


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