Privacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data

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dc.creatorKuijpers, Bart
dc.creatorLemmens, Vanessa
dc.creatorMoelans, Bart
dc.creatorTuyls, Karl
dc.date2008-03-11
dc.date.accessioned2026-07-07T12:17:32Z
dc.date.available2026-07-07T12:17:32Z
dc.descriptionWe 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.
dc.description25 pages
dc.identifierhttps://arxiv.org/abs/0803.1555
dc.identifierhttp://arxiv.org/abs/0803.1555
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212110
dc.subjectDatabases
dc.subjectMachine Learning
dc.subjectE.1; E.3; H.2.8; H.3.3
dc.titlePrivacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data
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