Privacy-preserving distributed mining of association rules on horizontally partitioned data
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Author
Christopher Clifton
Tech report number
CERIAS TR 2004-91
Entry type
article
Abstract
Data mining can extract important knowledge from large data collections ut sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. We address secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
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Date
2004 – 09
Address
Los Alamitos, CA
Journal
Transactions on Knowledge and Data Engineering
Key alpha
Clifton
Number
9
Pages
1026-1037
Publisher
IEEE Computer Society Press
Volume
16
Publication Date
2004-09-01
Language
English