One-Scan Sanitization of Collaborative Recommendation Association Rules

Abstract

For a given recommended item, a collaborative recommendation association rule set is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence priority. In this work, we propose an efficient one-scan sanitization algorithm to hide collaborative recommendation association rules. To hide association rules, previously proposed algorithms based on Apriori approach usually require multiple scanning of database to calculate the supports of the large itemsets. We propose here using a pattern-inversion tree to store related information so that only one scan of database is required. Numerical experiments show that the proposed algorithm out performs previous algorithms, with similar side effects.