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

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Jaideep Vaidya, Chris Clifton
• Keywords: Data Mining, Database Applications-Data Mining, Individual Transaction Data, Individual Transaction Values, KDD 2002