Privacy preserving association rule mining in vertically partitioned data

Jaideep Vaidya, Chris Clifton
2002 Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02  
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.
doi:10.1145/775107.775142 fatcat:ql6xkh2l5jcpjpzjhqzhmdeewi