# The power of synergy in differential privacy: Combining a small curator with local randomizers

arXiv: https://arxiv.org/abs/1912.08951 · HTML: https://mlsanity.com/paper/1912.08951/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free](https://mlsanity.com/paper/2305.09668/) (2023)
- [Mean Estimation Under Heterogeneous Privacy Demands](https://mlsanity.com/paper/2310.13137/) (2023)
- [Federated Learning with Heterogeneous Differential Privacy](https://mlsanity.com/paper/2110.15252/) (2021)
- [Can Membership Inferencing be Refuted?](https://mlsanity.com/paper/2303.03648/) (2023)
- [Transfer Learning In Differential Privacy's Hybrid-Model](https://mlsanity.com/paper/2201.12018/) (2022)

## Abstract (arXiv)

Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al., USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users. We focus on the regime where m << n and study the new capabilities of this (m,n)-hybrid model. We show that, despite the fact that the hybrid model adds no significant new capabilities for the basic task of simple hypothesis-testing, there are many other tasks (under a wide range of parameters) that can be solved in the hybrid model yet cannot be solved either by the curator or by the local-users separately. Moreover, we exhibit additional tasks where at least one round of interaction between the curator and the local-users is necessary -- namely, no hybrid model protocol without such interaction can solve these tasks. Taken together, our results show that the combination of the local model with a small curator can become part of a promising toolkit for designing and implementing differential privacy.