Collaborative Privacy Preserving Frequent Item Set Mining in Vertically Partitioned Databases

Abstract

Privacy concerns have become an important issue in Data Mining. This paper deal with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each individual database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each. We present two algorithms for discovering frequent item sets and analyze their security, privacy and complexity properties.