A New Approach for Detecting Anonymity of Patterns
Zhihui Wang, Wei Wang, Baile Shi · 2008
Information sharing becomes more frequently and easily than before. However, it also brings serious threats towards individual's privacy. It is no doubt that sharing personal data can cause privacy breaches. Moreover, sharing the knowledge discovered by data mining may also pose threats to personal privacy. In this paper, we consider the anonymity of patterns derived from the result of frequent itemset mining. A new projection-based approach for detecting anonymity of patterns is presented. We prove that the approach can detect all the maximal inference channels for non-k-anonymous patterns. The experimental results show that our approach is more efficient than previous work especially when the number of closed frequent itemsets in the mining result is close to or larger than the number of transactions in a database.