This repository is part of the SP-IA project, focusing on Secure and Privacy-Preserving Machine Learning. The main objectives of this project include:
- Conducting a survey on the security and privacy risks associated with various machine learning techniques.
- Analyzing and proposing potential countermeasures to enhance security and privacy protection.
- Selecting, implementing, and experimentally evaluating alternative approaches.
- Preparing a final report and scientific papers in a suitable format for publication.
Please refer to the documentation and resources provided in this repository for more information on the SP-IA project and its progress.
This repository covers a diverse range of resources and materials that have been carefully analised to support research and development in the field of security and privacy-preserving of machine learning.
- Notebooks: A collection of
Jupyter Notebooksdeveloped to support debugging and development within the project. - Modules: A set of
Pythonmodules intended for debugging and development assistance. - SGA: Code implementation for the SGA attack.
- SuperstarGAN: Code implementation for the SuperstarGAN model.
- TRM: Code implementation for the TRM attack.
This work is funded by FCT, through the project UIDB/04524/2020.