# The Differentially Private Lottery Ticket Mechanism

arXiv: https://arxiv.org/abs/2002.11613 · HTML: https://mlsanity.com/paper/2002.11613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing](https://mlsanity.com/paper/2305.01387/) (2023)

## Abstract (arXiv)

We propose the differentially private lottery ticket mechanism (DPLTM). An end-to-end differentially private training paradigm based on the lottery ticket hypothesis. Using "high-quality winners", selected via our custom score function, DPLTM significantly improves the privacy-utility trade-off over the state-of-the-art. We show that DPLTM converges faster, allowing for early stopping with reduced privacy budget consumption. We further show that the tickets from DPLTM are transferable across datasets, domains, and architectures. Our extensive evaluation on several public datasets provides evidence to our claims.