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.給定一個推薦項目，一個協同推薦關聯規則集是一組根據信賴值排序之最小關聯規則集，且具備相同之推薦結果。在本文中，我們提出一個有效率之一次掃瞄清除演算法以隱藏協同推薦關聯規則集。一般隱藏關聯規則之演算法皆須做多次之資料庫掃瞄。我們則提出一個利用式樣反轉樹(pattern-inverse tree)儲存相關資料並且只須掃瞄資料庫一次之演算法。數值實驗顯示我們所提之演算法比其他方法更有效率且具有類似之副作用。
