| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w25626
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| 来源ID | Working Paper 25626
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| A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples |
| Raj Chetty; John N. Friedman
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| 发表日期 | 2019-03-11
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| 出版年 | 2019
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| 语种 | 英语
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| 摘要 | 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. |
| 主题 | Econometrics
; Public Economics
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| URL | https://www.nber.org/papers/w25626
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/583300
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推荐引用方式
GB/T 7714 |
Raj Chetty,John N. Friedman. A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples. 2019.
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文件名：
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w25626.pdf
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格式：
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Adobe PDF
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