微数据中隐私的家族关联综合

Q2 Mathematics
Gary Benedetto, Evan Totty
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引用次数: 1

摘要

由于人口普查局努力在其所有产出中现代化其避免披露的努力,合成数据已成为一种成功的方式,为外部研究人员提供了对微数据进行各种分析的机会,同时仍然满足保护调查受访者隐私的法律目标。对研究人员来说,一些最有用的变量也是最难建模的变量:记录之间的关系。这些关系可以是家庭关系、家庭关系或雇主与雇员的关系等等。本文描述了一种将合成记录匹配在一起的方法,这种方法模仿了底层受保护数据中相关记录之间的协变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthesizing Familial Linkages for Privacy in Microdata
As the Census Bureau strives to modernize its disclosure avoidance efforts in all of its outputs, synthetic data has become a successful way to provide external researchers a chance to conduct a wide variety of analyses on microdata while still satisfying the legal objective of protecting privacy of survey respondents. Some of the most useful variables for researchers are some of the trickiest to model: relationships between records. These can be family relationships, household relationships, or employer-employee relationships to name a few. This paper describes a method to match synthetic records together in a way that mimics the covariation between related records in the underlying, protected data.
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来源期刊
Journal of Privacy and Confidentiality
Journal of Privacy and Confidentiality Computer Science-Computer Science (miscellaneous)
CiteScore
3.10
自引率
0.00%
发文量
11
审稿时长
24 weeks
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