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KDD
2002
ACM

Privacy preserving association rule mining in vertically partitioned data

9 years 11 months ago
Privacy preserving association rule mining in vertically partitioned data
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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-Data mining; H.2.4 [Database Management]: Systems-Distributed databases; H.2.7 [Database Management]: Database Administration--Security, integrity, and protection
Jaideep Vaidya, Chris Clifton
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2002
Where KDD
Authors Jaideep Vaidya, Chris Clifton
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