A collection of tools and resources for managing the statistical disclosure control of trained machine learning models. For a brief introduction, see Smith et al. (2022).
The sacroml package provides:
- A variety of privacy attacks for assessing machine learning models.
- The safemodel package: a suite of open source wrappers for common machine learning frameworks, including scikit-learn and Keras. It is designed for use by researchers in Trusted Research Environments (TREs) where disclosure control methods must be implemented. Safemodel aims to give researchers greater confidence that their models are more compliant with disclosure control.
$ pip install sacroml
Note: macOS users may need to install libomp due to a dependency on XGBoost:
$ brew install libomp
$ conda install sacroml
See the examples.
See API documentation.
See our contributing guide.
This work was funded by UK Research and Innovation under Grant Numbers MC_PC_21033 and MC_PC_23006 as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK). The specific projects were Semi-Automatic checking of Research Outputs (SACRO; MC_PC_23006) and Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER; MC_PC_21033).This project has also been supported by MRC and EPSRC [grant number MR/S010351/1]: PICTURES.
