# Synthetic Data Generators: Sequential and Private

arXiv: https://arxiv.org/abs/1902.03468 · HTML: https://mlsanity.com/paper/1902.03468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2019.

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

- [Private learning implies quantum stability](https://mlsanity.com/paper/2102.07171/) (2021)

## Abstract (arXiv)

We study the sample complexity of private synthetic data generation over an unbounded sized class of statistical queries, and show that any class that is privately proper PAC learnable admits a private synthetic data generator (perhaps non-efficient). Previous work on synthetic data generators focused on the case that the query class D is finite and obtained sample complexity bounds that scale logarithmically with the size |D|. Here we construct a private synthetic data generator whose sample complexity is independent of the domain size, and we replace finiteness with the assumption that D is privately PAC learnable (a formally weaker task, hence we obtain equivalence between the two tasks).