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- 2017
多属性泛化的K-匿名算法
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Abstract:
针对现有的K-匿名模型中存在泛化属性选取不唯一和数据过度泛化的问题，提出多属性泛化的K-匿名算法。在K-匿名模型实现的过程中，引入属性近似度概念，定量刻画准标识符属性的离散程度，进而确定泛化的准标识符属性；同时采用广度优先泛化的方法，避免数据被过度泛化，最终实现数据表的K-匿名要求。实验结果表明，多属性泛化的K-匿名模型可以提高泛化后数据精度，其处理效率和Datafly算法相当。该算法有效地解决了取值最多准标识符属性存在多个时的泛化属性选取问题，并且防止属性被过度泛化，提高数据的可用性。
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