This project has been put to rest.
http://web.archive.org/web/20201128195845/https://www.turing.ac.uk/research/research-projects/quantifying-uncertainty-and-preserving-privacy-synthetic-data-sets
Managing uncertainty in government modelling
Understanding the balance between utility, privacy and the uncertainty associated with synthetic data sets
UK
Sensitive datasets are often too inaccessible to make the most effective use of them (for example healthcare or census micro-data). Synthetic data โ artificially generated data used to replicate the statistical components of real-world data but without any identifiable information โ offers an altetnative. However, synthetic data is poorly understood in terms of how well it preserves the privacy of individuals on which the synthesis is based, and also of its utility (i.e. how representative of the underlying population the data are).
Resources

- Language(s)
- English
- Tech stack
- BuiltWith report โ
- Categories
- Artificial Intelligence (AI), Deep learning, Emerging tech, Emerging Tech Report Tools, Graveyard, Machine learning, The Tech
- Added
- 2021-06-22
- Last modified
- 2026-04-27T13:12:10.000Z
Additional details
- Number of integrations
- 0