Selected work

Representative Papers

PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)

May 02, 2025

This paper addresses privacy-preserving release of the 2020 U.S. Census Supplemental Demographic and Housing Characteristics (S-DHC) files. Method: It proposes the first zero-concentrated differential privacy (zCDP) framework designed for large-scale official statistics, introducing the discrete Gaussian mechanism for national-level statistical disclosure—rigorously proving its zCDP compliance—and implementing it via the Tumult Analytics platform to enable verifiable, production-grade privacy computation. Contribution/Results: (1) The first formally verified, production-deployed zCDP algorithm; (2) a demonstrable balance between strong privacy guarantees (zCDP) and high statistical utility on real-world census infrastructure; (3) a scalable, auditable privacy-enhancing paradigm for official statistics. The solution has been operationalized in the S-DHC data release system, marking a milestone in the U.S. Census Bureau’s privacy protection practice.

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

May 02, 2025

Fine-grained race/ethnicity statistics in the 2020 U.S. Census Detailed Demographic and Housing Characteristics File A (DHC-A) pose significant privacy risks due to re-identification vulnerabilities. Method: We propose the first differentially private framework for releasing multi-level geographic and cross-tabulated group statistics, featuring an adaptive granularity control mechanism that dynamically adjusts the number of statistics and the resolution of geographic and categorical dimensions based on group size, coupled with discrete Gaussian noise injection under zero-concentrated differential privacy (zCDP). Contribution/Results: Implemented and deployed via Tumult Analytics within the official census release pipeline, our approach achieves strong formal privacy guarantees (ε = 0.48 zCDP) while substantially improving statistical utility over prior methods. Empirically tuned and budget-validated, it balances practical deployability with regulatory compliance, establishing a scalable paradigm for privacy-preserving large-scale official statistics.

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

May 02, 2025

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.

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Recent publications

Latest Papers

PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)

May 02, 2025

This paper addresses privacy-preserving release of the 2020 U.S. Census Supplemental Demographic and Housing Characteristics (S-DHC) files. Method: It proposes the first zero-concentrated differential privacy (zCDP) framework designed for large-scale official statistics, introducing the discrete Gaussian mechanism for national-level statistical disclosure—rigorously proving its zCDP compliance—and implementing it via the Tumult Analytics platform to enable verifiable, production-grade privacy computation. Contribution/Results: (1) The first formally verified, production-deployed zCDP algorithm; (2) a demonstrable balance between strong privacy guarantees (zCDP) and high statistical utility on real-world census infrastructure; (3) a scalable, auditable privacy-enhancing paradigm for official statistics. The solution has been operationalized in the S-DHC data release system, marking a milestone in the U.S. Census Bureau’s privacy protection practice.

0 citationsRead paper

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

May 02, 2025

Fine-grained race/ethnicity statistics in the 2020 U.S. Census Detailed Demographic and Housing Characteristics File A (DHC-A) pose significant privacy risks due to re-identification vulnerabilities. Method: We propose the first differentially private framework for releasing multi-level geographic and cross-tabulated group statistics, featuring an adaptive granularity control mechanism that dynamically adjusts the number of statistics and the resolution of geographic and categorical dimensions based on group size, coupled with discrete Gaussian noise injection under zero-concentrated differential privacy (zCDP). Contribution/Results: Implemented and deployed via Tumult Analytics within the official census release pipeline, our approach achieves strong formal privacy guarantees (ε = 0.48 zCDP) while substantially improving statistical utility over prior methods. Empirically tuned and budget-validated, it balances practical deployability with regulatory compliance, establishing a scalable paradigm for privacy-preserving large-scale official statistics.

0 citationsRead paper

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

May 02, 2025

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.

0 citationsRead paper