计算机科学 ›› 2017, Vol. 44 ›› Issue (7): 42-46.doi: 10.11896/j.issn.1002-137X.2017.07.008
• 2016 年全国理论计算机科学学术年会 • 上一篇 下一篇
廖军,蒋朝惠,郭春,平源
LIAO Jun, JIANG Chao-hui, GUO Chun and PING Yuan
摘要： 为了在高效地保护数据隐私不被泄露的同时保证数据效用,提出了一种基于权重属性熵的分类匿名方法(Weight-properties Entropy for Classification Anonymous,WECA)。该方法在数据分类挖掘的特定应用背景下,通过信息熵的概念来计算数据集中不同准标识符属性对敏感属性的分类重要程度,选取分类权重属性熵比率最高的准标识符属性对分类树进行有利的划分,同时构建了分类匿名信息损失度量,在更好地保护隐私数据的前提下确保了数据分类效用。最后,在标准数据集上的实验结果表明,该算法在保证较少的匿名损失的同时具有较高的分类精度,提高了数据可用性。
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