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Applying Privacy Preserving Count Aggregate Queries to K-Classification

机译:将隐私保留将聚合查询应用于K-Classification

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It is important to process data effectively while preserving privacy of personal information. In this paper, we propose a technique to reconstruct results of count aggregate queries from a perturbed table for building a decision tree whose target attribute contains more than two classes. Using the conventional technique, we must reconstruct the results of target values from those of each value calculated independently in such the case. In this paper, we borrow and extend the conventional technique to reconstruct the results of target values at once. We also report some experimental results showing that our proposal can reduce reconstruction errors compared to the conventional technique in cases where perturbation ratio is high.
机译:在保留个人信息隐私时有效地处理数据非常重要。在本文中,我们提出了一种技术来重建来自扰动表的计数聚合查询结果,用于构建目标属性包含多种类别的决策树。使用传统技术,我们必须将目标值的结果从在这种情况下独立计算的每个值的结果重建。在本文中,我们借用并扩展了传统技术,一次重建目标值的结果。我们还报告了一些实验结果表明,与传统技术相比,我们的提案可以减少重建误差,因为在扰动比率高的情况下。

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