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.
Categories: cs.CR