# STAR: Secret Sharing for Private Threshold Aggregation Reporting

arXiv: https://arxiv.org/abs/2109.10074 · HTML: https://mlsanity.com/paper/2109.10074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2021.

## Built on by (most cited first, 2 of 2)

- [PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality](https://mlsanity.com/paper/2412.02340/) (2024)
- [Fluent: Round-efficient Secure Aggregation for Private Federated Learning](https://mlsanity.com/paper/2403.06143/) (2024)

## Abstract (arXiv)

Threshold aggregation reporting systems promise a practical, privacy-preserving solution for developers to learn how their applications are used "in-the-wild". Unfortunately, proposed systems to date prove impractical for wide scale adoption, suffering from a combination of requiring: i) prohibitive trust assumptions; ii) high computation costs; or 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 mathsfSTAR, a highly efficient, easily deployable system for providing cryptographically-enforced kappa-anonymity protections on user data collection. The mathsfSTAR 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 mathsfSTAR find that it is 1773× quicker, requires 62.4× less communication, and is 24× cheaper to run than the existing state-of-the-art.