科研之友
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摘要
k-anonymity is an effective method of privacy preserving. However, some traditional k-anonymity models do not capture diversity and dispersibility of sensitive values in each equivalence class, which makes the privacy disclosure of anonymity table occur easily. In this paper, an advanced (k, g)-anonymity model for numerical data is proposed and a (k, g)-MDAV algorithm is designed to achieve (k, g)-algorithm. Experimental results show that the algorithm can lower the risk of privacy disclosure while maintaining the data availability.
- 出版日期2014
- 单位福州大学
