Title :
Privacy-Preserving Distributed Decision Tree Learning with Boolean Class Attributes
Author :
Kikuchi, Hiroaki ; Ito, Kei ; Ushida, Mebae ; Tsuda, Hiroyuki ; Yamaoka, Yuji
Author_Institution :
Tokai Univ., Tokyo, Japan
Abstract :
This paper studies a privacy-preserving decision tree learning protocol (PPDT) for vertically partitioned datasets. In the vertically partitioned datasets, a single class (target) attribute are shared by both parities or carefully treated by either party in the existing studies. The proposed scheme allows both parties to have independent class attributes in secure way and to combine multiple class attributes in arbitrary boolean function, which gives parties a flexibility in data-mining. Our proposed PPDT protocol reduces the CPU intensive computation of logarithm by approximating with the piecewise linear function defined by light-weight fundamental operations of addition and constant-multiplication so that information gain for attribute can be evaluated in the secure function evaluation scheme. Using the UCI Machine Learning dataset and the synthesized dataset, the proposed protocol is evaluated in terms of the accuracy and the size of tree.
Keywords :
Boolean functions; cryptographic protocols; data mining; data privacy; decision trees; function approximation; function evaluation; learning (artificial intelligence); PPDT learning protocol; UCI machine learning dataset; addition operation; arbitrary Boolean function class attributes; constant-multiplication operation; data-mining; function evaluation scheme; information gain; logarithm CPU intensive computation reduction; piecewise linear function approximating; privacy-preserving decision tree learning protocol; single-class target attribute sharing; synthesized dataset; vertically partitioned datasets; Accuracy; Decision trees; Entropy; Partitioning algorithms; Piecewise linear approximation; Protocols; Vectors; data mining; decision tree; privacy;
Conference_Titel :
Advanced Information Networking and Applications (AINA), 2013 IEEE 27th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4673-5550-6
Electronic_ISBN :
1550-445X
DOI :
10.1109/AINA.2013.140