首页> 外文会议>33rd International Conference on Very Large Data Bases(VLDB 2007) >K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization
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K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization

机译:K-匿名化作为空间索引:迈向可扩展和增量式匿名化

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In this paper we observe that k-anonymizing a data set is strikingly similar to building a spatial index over the data set, so similar in fact that classical spatial indexing techniques can be used to anonymize data sets. We use this observation to leverage over 20 years of work on database indexing to provide efficient and dynamic anonymization techniques. Experiments with our implementation show that the R-tree index-based approach yields a batch anonymization algorithm that is orders of magnitude more efficient than previously proposed algorithms and has the advantage of supporting incremental updates. Finally, we show that the anonymizations generated by the R-tree approach do not sacrifice quality in their search for efficiency; in fact, by several previously proposed quality metrics, the compact partitioning properties of R-trees generate anonymizations superior to those generated by previously proposed anonymization algorithms.
机译:在本文中,我们观察到对数据集进行k匿名化与在数据集之上建立空间索引非常相似,因此实际上可以使用经典的空间索引技术对数据集进行匿名化。我们使用此观察结果来利用20多年的数据库索引工作来提供有效和动态的匿名化技术。使用我们的实现的实验表明,基于R树索引的方法可产生批处理匿名化算法,该算法比以前提出的算法效率高几个数量级,并且具有支持增量更新的优势。最后,我们证明了R树方法生成的匿名化并没有牺牲质量来提高效率。实际上,通过几个先前提出的质量度量,R树的紧凑分区属性所产生的匿名化效果优于先前提出的匿名化算法所产生的匿名化效果。

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