An Incremental Algorithm for Mining Privacy-Preserving Frequent Itemsets

Abstract

Privacy preserving data mining is a novel research direction in data mining and statistical database, where data mining algorithms are analyzed for the side-effects they incur in data privacy. There have been many studies on efficient discovery of frequent itemsets in privacy preserving data mining. However, it is nontrivial to maintain such discovered frequent itemsets because a database may allow frequent itemsets updates and such frequent itemsets may be turned into infrequent itemsets. In this paper, an incremental updating algorithm IPPFIM is proposed for efficient maintenance of discovered frequent itemsets when new transaction data are added to a transaction database in privacy preserving. The algorithm makes use of previous mining results to cut down the cost of finding new frequent itemsets in an updated database, the performance evaluation shows the efficiency of this method