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Variance Modeling for Differentially Private Counts

Abstract Number:

3028 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Kyle Irimata (1)

Institutions:

(1) U.S. Census Bureau, Washington, DC

First Author:

Kyle Irimata  
U.S. Census Bureau

Presenting Author:

Kyle Irimata  
N/A

Abstract Text:

The Census Bureau adopted differential privacy (DP) as implemented through the TopDown Algorithm (TDA) for the 2020 Decennial Census in order to protect respondent confidentiality. Though the variances of the additive DP noise are publicly available, the impacts of postprocessing in the TDA to ensure various quality metrics, such as hierarchical consistency and non-negativity are met are less easily quantified as the unprotected counts are not publicly available for 2020 data. In this work, we investigate the use of a small area estimation approach to strengthen estimates of variability obtained using the 2010 demonstration products, as compared to the official 2010 redistricting file. We propose using a grouping of similar geographies to obtain estimates of variance from the 2010 data, and to incorporate a modified generalized variance function to improve the estimates for 2020.

Keywords:

Small Area Estimation|Differential Privacy|Generalized variance function| | |

Sponsors:

Survey Research Methods Section

Tracks:

Data Analysis/Modeling

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No

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I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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