Privacy in statistical databases. PSD (Conference : 2004- )最新文献

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Privacy Analysis with a Distributed Transition System and a Data-Wise Metric 基于分布式转换系统和数据明智度量的隐私分析

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_2

S. Anantharaman, S. Frittella, Benjamin Nguyen

引用次数: 0

Automatic Evaluation of Disclosure Risks of Text Anonymization Methods 文本匿名化方法披露风险的自动评估

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_12

Benet Manzanares-Salor, David Sánchez, Pierre Lison

引用次数: 0

Explaining Recurrent Machine Learning Models: Integral Privacy Revisited 解释循环机器学习模型:重新审视积分隐私

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-23 DOI: 10.1007/978-3-030-57521-2_5

V. Torra, G. Navarro-Arribas, Edgar Galván López

引用次数: 1

Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data 与公共信息一致的私人后验推断——基于综合普查数据的小区域估计案例研究

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-23 DOI: 10.1007/978-3-030-57521-2_23

Jeremy Seeman, A. Slavkovic, M. Reimherr

引用次数: 11

Bayesian Modeling for Simultaneous Regression and Record Linkage 同时回归和记录链接的贝叶斯建模

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-23 DOI: 10.1007/978-3-030-57521-2_15

Jiurui Tang, Jerome P. Reiter, R. Steorts

引用次数: 7

A Bayesian Nonparametric Approach to Differentially Private Data 差分私有数据的贝叶斯非参数方法

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-16 DOI: 10.1007/978-3-030-57521-2_3

Fadhel Ayed, M. Battiston, G. Benedetto

引用次数: 0

Advantages of Imputation vs. Data Swapping for Statistical Disclosure Control 统计披露控制的归算与数据交换优势

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-15 DOI: 10.1007/978-3-030-57521-2_20

Satkartar K. Kinney, Charlotte Looby, Feng Yu

引用次数: 0

On Different Formulations of a Continuous CTA Model. 关于连续CTA模型的不同表述。

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-01 Epub Date: 2020-09-16 DOI: 10.1007/978-3-030-57521-2_12

Goran Lesaja, Ionut Iacob, Anna Oganian

In this paper, we consider a Controlled Tabular Adjustment (CTA) model for statistical disclosure limitation of tabular data. The goal of the CTA model is to find the closest safe (masked) table to the original table that contains sensitive information. The measure of closeness is usually measured using ℓ ₁ or ℓ ₂ norm. However, in the norm-based CTA model, there is no control of how well the statistical properties of the data in the original table are preserved in the masked table. Hence, we propose a different criterion of "closeness" between the masked and original table which attempts to minimally change certain statistics used in the analysis of the table. The Chi-square statistic is among the most utilized measures for the analysis of data in two-dimensional tables. Hence, we propose a Chi-square CTA model which minimizes the objective function that depends on the difference of the Chi-square statistics of the original and masked table. The model is non-linear and non-convex and therefore harder to solve which prompted us to also consider a modification of this model which can be transformed into a linear programming model that can be solved more efficiently. We present numerical results for the two-dimensional table illustrating our novel approach and providing a comparison with norm-based CTA models.

在本文中，我们考虑一个控制表格调整(CTA)模型的统计披露限制的表格数据。CTA模型的目标是找到最接近包含敏感信息的原始表的安全(掩码)表。接近度的度量通常用1或2范数来度量。然而，在基于规范的CTA模型中，无法控制原始表中数据的统计属性在掩码表中的保存程度。因此，我们提出了一种不同的“接近”标准，在蒙面表和原始表之间，它试图最小限度地改变表分析中使用的某些统计数据。卡方统计量是二维表中数据分析最常用的方法之一。因此，我们提出了一个卡方CTA模型，该模型最小化了依赖于原始表和掩码表的卡方统计量差异的目标函数。该模型是非线性和非凸的，因此更难求解，这促使我们也考虑对该模型进行修改，将其转换为可以更有效地求解的线性规划模型。我们给出了二维表格的数值结果，说明了我们的新方法，并提供了与基于规范的CTA模型的比较。

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引用次数: 1

Multivariate Top-Coding for Statistical Disclosure Limitation. 统计披露限制的多变量顶部编码。

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-09-01 Epub Date: 2020-09-16 DOI: 10.1007/978-3-030-57521-2_10

Anna Oganian, Ionut Iacob, Goran Lesaja

One of the most challenging problems for national statistical agencies is how to release to the public microdata sets with a large number of attributes while keeping the disclosure risk of sensitive information of data subjects under control. When statistical agencies alter microdata in order to limit the disclosure risk, they need to take into account relationships between the variables to produce a good quality public data set. Hence, Statistical Disclosure Limitation (SDL) methods should not be univariate (treating each variable independently of others), but preferably multivariate, that is, handling several variables at the same time. Statistical agencies are often concerned about disclosure risk associated with the extreme values of numerical variables. Thus, such observations are often top or bottom-coded in the public use files. Top-coding consists of the substitution of extreme observations of the numerical variable by a threshold, for example, by the 99th percentile of the corresponding variable. Bottom coding is defined similarly but applies to the values in the lower tail of the distribution. We argue that a univariate form of top/bottom-coding may not offer adequate protection for some subpopulations which are different in terms of a top-coded variable from other subpopulations or the whole population. In this paper, we propose a multivariate form of top-coding based on clustering the variables into groups according to some metric of closeness between the variables and then forming the rules for the multivariate top-codes using techniques of Association Rule Mining within the clusters of variables obtained on the previous step. Bottom-coding procedures can be defined in a similar way. We illustrate our method on a genuine multivariate data set of realistic size.

国家统计机构面临的最具挑战性的问题之一是，如何向公众发布具有大量属性的微观数据集，同时控制数据主体敏感信息的披露风险。当统计机构为了限制披露风险而更改微观数据时，他们需要考虑变量之间的关系，以生成高质量的公共数据集。因此，统计披露限制（SDL）方法不应是单变量的（独立于其他变量处理每个变量），而最好是多变量的，即同时处理多个变量。统计机构经常关注与数字变量极值相关的披露风险。因此，此类观察结果通常在公共使用文件中进行顶部或底部编码。顶部编码包括用阈值替换数值变量的极端观测值，例如用相应变量的第99个百分位数。底部编码的定义类似，但适用于分布的下尾部中的值。我们认为，单变量形式的顶部/底部编码可能无法为某些亚群体提供足够的保护，这些亚群体在顶部编码变量方面与其他亚群体或整个群体不同。在本文中，我们提出了一种多元顶端编码形式，该形式基于根据变量之间的一些接近度度量将变量聚类成组，然后在前一步获得的变量聚类中使用关联规则挖掘技术形成多元顶端编码的规则。底部编码过程可以用类似的方式定义。我们在真实大小的真实多元数据集上说明了我们的方法。

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引用次数: 0

Statistical Disclosure Control When Publishing on Thematic Maps 专题地图出版时的统计资料披露管制

Privacy in statistical databases. PSD (Conference : 2004- )

Pub Date : 2020-07-14 DOI: 10.1007/978-3-030-57521-2_14

Douwe Hut, J. Goseling, M. V. Lieshout, P. D. Wolf, E. D. Jonge

引用次数: 1

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