Metadata-Version: 2.1
Name: aisdc
Version: 1.2.0
Summary: Tools for the statistical disclosure control of machine learning models
Home-page: https://github.com/AI-SDC/AI-SDC
Maintainer: Jim Smith
Maintainer-email: james.smith@uwe.ac.uk
License: MIT
Project-URL: Changelog, https://github.com/AI-SDC/AI-SDC/CHANGELOG.md
Project-URL: Documentation, https://ai-sdc.github.io/AI-SDC/
Project-URL: Bug Tracker, https://github.com/AI-SDC/AI-SDC/issues
Project-URL: Discussions, https://github.com/AI-SDC/AI-SDC/discussions
Keywords: data-privacy,data-protection,machine-learning,privacy,privacy-tools,statistical-disclosure-control
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Operating System :: OS Independent
Requires-Python: <3.12,>=3.9
Description-Content-Type: text/markdown
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Requires-Dist: fpdf
Requires-Dist: pypdf
Requires-Dist: multiprocess
Requires-Dist: scikit-learn
Requires-Dist: xgboost
Provides-Extra: safemodel
Requires-Dist: tensorflow<2.15,>=2.14; extra == "safemodel"
Requires-Dist: tensorflow-privacy==0.9.0; extra == "safemodel"
Requires-Dist: dictdiffer; extra == "safemodel"
Provides-Extra: doc
Requires-Dist: tensorflow<2.15,>=2.14; extra == "doc"
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Requires-Dist: tensorflow<2.15,>=2.14; extra == "test"
Requires-Dist: tensorflow-privacy==0.9.0; extra == "test"
Requires-Dist: dictdiffer; extra == "test"
Requires-Dist: pytest; extra == "test"
Requires-Dist: pytest-cov; extra == "test"

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# AI-SDC

A collection of tools and resources for managing the [statistical disclosure control](https://en.wikipedia.org/wiki/Statistical_disclosure_control) of trained [machine learning](https://en.wikipedia.org/wiki/Machine_learning) models. For a brief introduction, see [Smith et al. (2022)](https://doi.org/10.48550/arXiv.2212.01233).

The `aisdc` 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](https://scikit-learn.org) and [Keras](https://keras.io). 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.

## Installation

[![PyPI package](https://img.shields.io/pypi/v/aisdc.svg)](https://pypi.org/project/aisdc)

Install `aisdc` and manually copy the [`examples`](examples/).

To install only the base package, which includes the attacks used for assessing privacy:

```
$ pip install aisdc
```

To additionally install the safemodel package:

```
$ pip install aisdc[safemodel]
```

Note: macOS users may need to install libomp due to a dependency on XGBoost:
```
$ brew install libomp
```

## Running

See the [`examples`](examples/).

## Acknowledgement

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](https://dareuk.org.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.

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