# Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics

arXiv: https://arxiv.org/abs/2108.01521 · HTML: https://mlsanity.com/paper/2108.01521/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [Privacy-Aware Compression for Federated Data Analysis](https://mlsanity.com/paper/2203.08134/) (2022)
- [Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings](https://mlsanity.com/paper/2206.00807/) (2022)
- [Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy](https://mlsanity.com/paper/2302.09624/) (2023)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)

## Abstract (arXiv)

Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's trust and full backing of mobile platforms. In this work, we propose numerical aggregation protocols that empirically improve upon prior art, while providing comparable local differential privacy guarantees. Sharing a single private bit per value supports privacy metering that enable privacy controls and guarantees that are not covered by differential privacy. We put emphasis on the ease of implementation, compatibility with existing methods, and compelling empirical performance.