Enhancing the efficiency in privacy preserving learning of decision trees in partitioned databases
作者： Peter Lory
DOI: 10.1007/978-3-642-33627-0_25
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摘要: This paper considers a scenario where two parties having private databases wish to cooperate by computing data mining algorithm on the union of their without revealing any unnecessary information. In particular, they want apply decision tree learning ID3 in privacy preserving manner. Lindell and Pinkas (2002) have presented protocol for this purpose, which enjoys formal proof is considerably more efficient than generic solutions. The crucial point approximation logarithm function truncated Taylor series. present improves using suitable Chebyshev expansion. approach results new version protocol.
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sci-hub.st 参考文章(21)
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