The Classification of k-anonymity Data
Lin Bingchun, Liu Guohua · 2011
In recent years, anonymization methods have emerged as an important tool to preserver individual privacy when releasing privacy sensitive data. All of these methods are under different privacy and utility assumption. But there has been little research addressing how to effectively use the anonymized data for data mining. Data mining is one of problems for the utility of anonymized data under the k-anonymity privacy protection model. In this paper, we propose a decision tree algorithm based on k-anonymity. The algorithm accepts the k-anonymity table as input, directly. To avoid the ID3 algorithm data preparation work before running. Experimental results show that there are significantly improved. At last, we use the decision tree to classify the k-anonymity data. Experimental results show that it is effective.