Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
Authors: Kaitlyn Webb (Pennsylvania State University), Prottay Protivash (Pennsylvania State University), John Durrell (Pennsylvania State University), Aleksandra Slavkovic (Pennsylvania State University), Daniel Kifer (Pennsylvania State University), Daniell Toth (National Institute of Statistical Sciences)
Volume: 2026
Issue: 3
Pages: 646–687
DOI: https://doi.org/10.56553/popets-2026-0100
Abstract: Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Standard formal privacy techniques for person-level data, like differential privacy, are designed to protect against membership inference and hence 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. Prior proposals, therefore, took a personalized differential privacy approach that allowed privacy parameters to degrade for the outlying records – larger establishments get weaker membership inference guarantees. However, providing guarantees to some entities that are strictly weaker than guarantees for others is problematic from a policy standpoint. In this paper, we propose a novel confidentiality framework for business data with a focus on interpretability for policy makers. Instead of protecting against membership inference, which is often not a concern in business data, we protect against attribute inferences that are too precise. In our framework, data curators specify a neighbor function that is used to define uncertainty interval bands around an establishment’s attribute values and the privacy parameters govern the strength of indistinguishability between values within the same uncertainty interval. We propose two query-answering mechanisms under this framework and evaluate them on: (1) a confidential Quarterly Census of Employment and Wages (QCEW) dataset produced by the U.S. Bureau of Labor Statistics (this was done through a cooperative agreement), and (2) a substitute dataset that we created from public sources (and will publicly release).
Keywords: data privacy, formal privacy, establishment data
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