# Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

arXiv: https://arxiv.org/abs/1704.08065 · HTML: https://mlsanity.com/paper/1704.08065/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [Topology-Based Reconstruction Prevention for Decentralised Learning](https://mlsanity.com/paper/2312.05248/) (2023)
- [Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning](https://mlsanity.com/paper/2605.19969/) (2026)
- [Defining Decentralization: An Ontological Perspective](https://mlsanity.com/paper/2608.09748/) (2026)

## Abstract (arXiv)

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems.