标题： 面向机构数据的统计友好保密保护，应用于QCEW 显示英文标题

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

摘要： 商业数据的保密性是披露回避中研究不足的领域，其中传统方法难以提供可接受的结果。 现代为个人级数据设计的正式隐私技术由于商业数据的高度偏斜性质，且极端异常记录往往是查询答案的重要贡献者，因此无法提供适当的保密性/效用权衡。 在本文中，受高斯差分隐私的启发，我们提出了一种针对商业数据的新保密框架，并重点关注政策制定者的可解释性。 我们提出了两种查询回答机制，并分析了当噪声查询答案转换为保留保密性的微观数据时出现的新挑战。 我们在保密的季度就业和工资普查（QCEW）微观数据和一个公开替代数据集上评估了我们的机制。 显示英文摘要

摘要： 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.