Privacy preserving association rule mining in vertically partitioned data
Published 23 July 2002
Jaideep Vaidya, Chris Clifton
Citations1,009
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TL;DR
This paper presents a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.
Abstract
Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.
Keywords
Computer Science
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