Efficient Hiding of Collaborative Recommendation Association Rules with Updates

We propose here an efficient data mining algorithm to hide collaborative recommendation association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicted item, a collaborative recommendation association rule set [10] is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence priority. Several approaches to hide collaborative recommendation association rules from static databases have been proposed [10, 11]. However, frequent updates to the database may require repeated sanitizations of original database and added data sets. The efforts of previous sanitization are not utilized in these approaches. In this work, we propose using pattern inversion tree to store the added data set in one database scan. It is then sanitized and merged to the original sanitized database. Numerical experiments show that the proposed approach out performs the direct sanitization approach on original and added data sets, with similar side effects.