Publication
Title
Privacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data
Author
Abstract
We consider privacy preserving decision tree induction via ID3 in the case where the training data is horizontally or vertically distributed. Furthermore, we consider the same problem in the case where the data is both horizontally and vertically distributed, a situation we refer to as grid partitioned data. We give an algorithm for privacy preserving ID3 over horizontally partitioned data involving more than two parties. For grid partitioned data, we discuss two different evaluation methods for preserving privacy ID3, namely, first merging horizontally and developing vertically or first merging vertically and next developing horizontally. Next to introducing privacy preserving data mining over grid-partitioned data, the main contribution of this paper is that we show, by means of a complexity analysis that the former evaluation method is the more efficient.
Language
English
Publication
arxiv , 2008
DOI
10.48550/ARXIV.0803.1555FOCUSTOLEARNMORE
Volume/pages
25 p.
Full text (Publisher's DOI)
UAntwerpen
Research group
Publication type
Subject
External links
Record
Identifier
Creation 10.01.2024
Last edited 10.01.2024
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