SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)

2025-05-02

AI Summary

This work addresses the privacy-preserving release of the 2020 U.S. Census Detailed Demographic and Housing Characteristics File B (DHC-B), a high-dimensional, nationally representative dataset with complex hierarchical geography and household-level attributes (e.g., household type, tenure status, householder race/ethnicity/tribal affiliation). Method: We design and deploy the first zero-concentrated differential privacy (zCDP) system for national census data release. Our approach introduces a discrete Gaussian noise injection mechanism tailored to multi-level geographic nesting and fine-grained household statistics, providing rigorous zCDP guarantees. Built atop the Tumult Analytics privacy computing library, the system implements a scalable tabulation pipeline with theoretically bounded error. Contribution/Results: The system achieves a superior utility-privacy trade-off and has been adopted to produce the official DHC-B data products—the first successful large-scale deployment of zCDP for detailed national census data release.

Technology Category

• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detailed DHC-B) as part of the 2020 Census. The tabulations contain household statistics about household type and tenure iterated by the householder's detailed race, ethnicity, or American Indian and Alaska Native tribe and village at varying levels of geography. We describe the algorithmic strategy which is based on adding noise from a discrete Gaussian distribution and show that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We discuss how the implementation of the SafeTab-H codebase relies on the Tumult Analytics privacy library. We also describe the theoretical expected error properties of the algorithm and explore various aspects of its parameter tuning.

Problem

• Develops SafeTab-H for Census data privacy
• Ensures differential privacy in demographic statistics
• Optimizes algorithm parameters for accuracy

Innovation

• Uses discrete Gaussian noise addition
• Implements zero-concentrated differential privacy
• Relies on Tumult Analytics privacy library