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Activity Number:
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591
- Synthetic Data and Data Disclosure
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Government Statistics Section
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Abstract #329812
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Presentation
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Title:
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Preserving Privacy in Person-Level Data for the American Community Survey
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Author(s):
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Michael H. Freiman* and Rolando A. RodrÃguez and Jerome P. Reiter and Amy D. Lauger
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Companies:
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U.S. Census Bureau and U.S. Census Bureau and Duke University and U.S. Census Bureau
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Keywords:
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privacy;
confidentiality;
synthetic data;
survey data
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Abstract:
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The Census Bureau is researching model-based synthetic person-level data that maintain many of the properties of the original American Community Survey (ACS) data while protecting individual privacy. Protecting the ACS while maintaining data quality presents particular challenges because of the ACS's sample weighting, the survey's large number of variables and the small geographies for which ACS data are desired. This paper discusses the reasons adapting existing differential privacy methods is difficult and the describes the approaches we are investigating to protect the data, including tree-based methods for categorical or discrete variables and regression for continuous variables.
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Authors who are presenting talks have a * after their name.
