摘要翻译：
微观数据统计披露控制(SDC)的目的。数据匿名或隐私保护数据挖掘，是将包含单个应答者答案的数据集发布，这样发布的记录对应的应答者就不能被重新识别，发布的数据在分析上是有用的。SDC方法要么是基于掩蔽原始数据，生成它们的合成版本，要么是结合原始数据和合成数据创建混合版本。分类数据特别是名义数据的SDC方法的选择要比数值数据的方法选择小得多。我们通过为分级标称数据引入一个数字映射来缓解这个问题，该映射允许对它们进行计算手段、方差和协方差。
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英文标题：
《Marginality: a numerical mapping for enhanced treatment of nominal and
  hierarchical attributes》
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作者：
Josep Domingo-Ferrer
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data anonymization or privacy-preserving data mining, is to publish data sets containing the answers of individual respondents in such a way that the respondents corresponding to the released records cannot be re-identified and the released data are analytically useful. SDC methods are either based on masking the original data, generating synthetic versions of them or creating hybrid versions by combining original and synthetic data. The choice of SDC methods for categorical data, especially nominal data, is much smaller than the choice of methods for numerical data. We mitigate this problem by introducing a numerical mapping for hierarchical nominal data which allows computing means, variances and covariances on them.
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PDF链接：
https://arxiv.org/pdf/1202.6009