摘要翻译：
统计机构收集的企业数据很难得到保护。许多企业具有独特的特点，就业、销售和利润的分布高度倾斜。希望进行身份识别攻击的攻击者通常比任何个人都能够获得更多的信息。因此，大多数避免披露机制未能在有用性和保密保护之间取得可接受的平衡。按地理或详细的行业类别进行的详细汇总统计很少，公众使用的企业微观数据几乎不存在，获取机密的微观数据可能是一个负担。合成微数据作为一种安全的微数据发布机制被提出，作为如何向研究人员提供更广泛的此类数据集访问的更广泛讨论的一部分。在本文中，我们记录了一个实验，以创建分析有效的合成数据，使用完全相同的模型和方法，以前在美国使用，数据来自两个不同的国家：加拿大(LEAP)和德国(BHP)。我们评估了效用和保护，并提供了一个评估的可行性，以一种成本效益的方式将这种方法扩展到其他数据。
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英文标题：
《Applying Data Synthesis for Longitudinal Business Data across Three
  Countries》
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作者：
M. Jahangir Alam and Benoit Dostie and J\"org Drechsler and Lars
  Vilhuber
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Data on businesses collected by statistical agencies are challenging to protect. Many businesses have unique characteristics, and distributions of employment, sales, and profits are highly skewed. Attackers wishing to conduct identification attacks often have access to much more information than for any individual. As a consequence, most disclosure avoidance mechanisms fail to strike an acceptable balance between usefulness and confidentiality protection. Detailed aggregate statistics by geography or detailed industry classes are rare, public-use microdata on businesses are virtually inexistant, and access to confidential microdata can be burdensome. Synthetic microdata have been proposed as a secure mechanism to publish microdata, as part of a broader discussion of how to provide broader access to such data sets to researchers. In this article, we document an experiment to create analytically valid synthetic data, using the exact same model and methods previously employed for the United States, for data from two different countries: Canada (LEAP) and Germany (BHP). We assess utility and protection, and provide an assessment of the feasibility of extending such an approach in a cost-effective way to other data.
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PDF链接：
https://arxiv.org/pdf/2008.02246