  Threshold aggregation reporting systems promise a practical,
privacy-preserving solution for developers to learn how their applications are
used "\emph{in-the-wild}". Unfortunately, proposed systems to date prove
impractical for wide scale adoption, suffering from a combination of requiring:
\emph{i)} prohibitive trust assumptions; \emph{ii)} high computation costs; or
\emph{iii)} massive user bases. As a result, adoption of truly-private
approaches has been limited to only a small number of enormous (and enormously
costly) projects.
  In this work, we improve the state of private data collection by proposing
$\mathsf{STAR}$, a highly efficient, easily deployable system for providing
cryptographically-enforced $\kappa$-anonymity protections on user data
collection. The $\mathsf{STAR}$ protocol is easy to implement and cheap to run,
all while providing privacy properties similar to, or exceeding the current
state-of-the-art. Measurements of our open-source implementation of
$\mathsf{STAR}$ find that it is $1773\times$ quicker, requires $62.4\times$
less communication, and is $24\times$ cheaper to run than the existing
state-of-the-art.
