Publishing Sensitive Transactions for Itemset Utility

We consider the problem of publishing sensitive transaction data with privacy preservation. High dimensionality of transaction data poses unique challenges on data privacy and data utility. On one hand, re-identification attacks tend to use a subset of items that infrequently occur in transactions, called moles. On the other hand, data mining applications typically depend on subsets of items that frequently occur in transactions, called nuggets. Thus the problem is how to eliminate all moles while retaining nuggets as much as possible. A challenge is that moles and nuggets are multi-dimensional with exponential growth and are tangled together by shared items. We present a novel and scalable solution to this problem. The novelty lies in a compact border data structure that eliminates the need of generating all moles and nuggets.