Many privacy preserving data mining algorithms attempt to selectively hide what database owners consider as sensitive. Specifically, in the association-rules domain, many of these algorithms are based on item-restriction methods; that is, removing items from some transactions in order to hide sensitive frequent itemsets.
The infancy of this area has not produced clear methods neither evaluated those few available. However, determining what is most effective in protecting sensitive itemsets while not hiding non-sensitive ones as a side effect remains a crucial research issue. This paper introduces two new techniques that deal with scenarios where many itemsets of different sizes are sensitive. We empirically evaluate our two sanitization techniques and compare their efficiency as well as which has the minimum effect on the non-sensitive frequent itemsets.