# Fog Computing as Privacy Enabler

arXiv: https://arxiv.org/abs/1910.04032 · HTML: https://mlsanity.com/paper/1910.04032/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

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

- [FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning](https://mlsanity.com/paper/2501.19091/) (2025)
- [Revealing the influence of participant failures on model quality in cross-silo Federated Learning](https://mlsanity.com/paper/2603.25289/) (2026)

## Abstract (arXiv)

Despite broad discussions on privacy challenges arising from fog computing, the authors argue that privacy and security requirements might actually drive the adoption of fog computing. They present four patterns of fog computing fostering data privacy and the security of business secrets, complementing existing cryptographic approaches. Their practical application is illuminated on the basis of three case studies.