# Multi-Central Differential Privacy

arXiv: https://arxiv.org/abs/2009.05401 · HTML: https://mlsanity.com/paper/2009.05401/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

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

- [Algorithms with More Granular Differential Privacy Guarantees](https://mlsanity.com/paper/2209.04053/) (2022)
- [Distributed Differentially Private Data Analytics via Secure Sketching](https://mlsanity.com/paper/2412.00497/) (2024)
- [Differential Privacy on Trust Graphs](https://mlsanity.com/paper/2410.12045/) (2024)

## Abstract (arXiv)

Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their privacy. A popular alternative is the local model in which the aggregator is untrusted and instead each individual is responsible for their own privacy. The decentralized privacy guarantee of the local model comes at a high price in statistical utility or computational complexity. Thus intermediate models such as the shuffled model and pan privacy have been studied in an attempt to attain the best of both worlds. In this note, we propose an intermediate trust model for differential privacy, which we call the multi-central model. Here there are multiple aggregators and we only assume that they do not collude nefariously. This model relaxes the trust requirements of the central model while avoiding the price of the local model. We motivate this model and provide some simple and efficient algorithms for it. We argue that this model is a promising direction for further research.