Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW

• URL: http://arxiv.org/abs/2509.01597v1
• Date: Mon, 01 Sep 2025 16:29:54 GMT
• Title: Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
• Authors: Kaitlyn Webb, Prottay Protivash, John Durrell, Daniell Toth, Aleksandra Slavković, Daniel Kifer,
• Abstract summary: We propose a novel confidentiality framework for business data with a focus on interpretability for policy makers.<n>We analyze new challenges that arise when noisy query answers are converted into confidentiality-preserving microdata.
• Score: 39.69299537637253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Modern formal privacy techniques designed for person-level data do not provide suitable confidentiality/utility trade-offs due to the highly skewed nature of business data and because extreme outlier records are often important contributors to query answers. In this paper, inspired by Gaussian Differential Privacy, we propose a novel confidentiality framework for business data with a focus on interpretability for policy makers. We propose two query-answering mechanisms and analyze new challenges that arise when noisy query answers are converted into confidentiality-preserving microdata. We evaluate our mechanisms on confidential Quarterly Census of Employment and Wages (QCEW) microdata and a public substitute dataset.

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