  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.

Econometrics