Privacy preserving decision tree learning over multiple parties

Autor:	Fatih Emekci, Divyakant Agrawal, O. D. Sahin, A. El Abbadi
Rok vydání:	2007
Předmět:	Incremental decision tree Information Systems and Management Computer science Data stream mining Decision tree learning Data quality Decision tree ID3 algorithm Data mining computer.software_genre Communication complexity computer Data warehouse
Zdroj:	Data & Knowledge Engineering. 63:348-361
ISSN:	0169-023X
DOI:	10.1016/j.datak.2007.02.004
Popis:	Data mining over multiple data sources has emerged as an important practical problem with applications in different areas such as data streams, data-warehouses, and bioinformatics. Although the data sources are willing to run data mining algorithms in these cases, they do not want to reveal any extra information about their data to other sources due to legal or competition concerns. One possible solution to this problem is to use cryptographic methods. However, the computation and communication complexity of such solutions render them impractical when a large number of data sources are involved. In this paper, we consider a scenario where multiple data sources are willing to run data mining algorithms over the union of their data as long as each data source is guaranteed that its information that does not pertain to another data source will not be revealed. We focus on the classification problem in particular and present an efficient algorithm for building a decision tree over an arbitrary number of distributed sources in a privacy preserving manner using the ID3 algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d5f67151899a9c42ddd43cebdf39d3ad https://doi.org/10.1016/j.datak.2007.02.004 Zobrazit plný text záznamu Full Text from ScienceDirect