  We study the problem of differentially private synthetic data generation for
hierarchical datasets in which individual data points are grouped together
(e.g., people within households). In particular, to measure the similarity
between the synthetic dataset and the underlying private one, we frame our
objective under the problem of private query release, generating a synthetic
dataset that preserves answers for some collection of queries (i.e., statistics
like mean aggregate counts). However, while the application of private
synthetic data to the problem of query release has been well studied, such
research is restricted to non-hierarchical data domains, raising the initial
question -- what queries are important when considering data of this form?
Moreover, it has not yet been established how one can generate synthetic data
at both the group and individual-level while capturing such statistics. In
light of these challenges, we first formalize the problem of hierarchical query
release, in which the goal is to release a collection of statistics for some
hierarchical dataset. Specifically, we provide a general set of statistical
queries that captures relationships between attributes at both the group and
individual-level. Subsequently, we introduce private synthetic data algorithms
for hierarchical query release and evaluate them on hierarchical datasets
derived from the American Community Survey and Allegheny Family Screening Tool
data. Finally, we look to the American Community Survey, whose inherent
hierarchical structure gives rise to another set of domain-specific queries
that we run experiments with.
