A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples
A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples
复制标题
小样本统计数据披露时减少隐私损失的实用方法
DOI：
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复制
发表时间：
2019
影响因子：
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通讯作者：
John N Friedman
中科院分区：
文献类型：
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作者：
Raj Chetty;John N Friedman
我们开发了一种简单的方法，以在公开统计数据（例如基于少量观察样本的 OLS 回归估计）时减少隐私损失。我们关注数据集可以分为许多组（“单元”）并且有兴趣发布其中一个或多个单元的统计数据的情况。基于差分隐私文献的想法，我们按照统计的最大观察灵敏度的比例向感兴趣的统计中添加噪声，该灵敏度定义为在数据中的所有单元格中添加或删除单个观察所导致的统计的最大变化。直观上，我们的方法允许通过在估计中添加足够的噪声来保护隐私，从而以任意小样本发布统计数据。尽管我们的方法没有提供正式的隐私保证，但在隐私损失和统计偏差方面，它通常优于广泛使用的披露限制方法，例如基于计数的细胞抑制。我们通过讨论如何使用该方法来发布机会地图集中人口普查区的社会流动性估计值来说明如何实施该方法。我们还提供分步指南和说明性 Stata 代码来实施我们的方法。
We develop a simple method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on samples with small numbers of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic's maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.
影响因子：
10.7
作者：
Abowd, John M.;Schmutte, Ian M.
通讯作者：
Schmutte, Ian M.