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SP-IA: Secure & Privacy-Preserving Machine Learning

Description

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.

Content Overview

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 Notebooks developed to support debugging and development within the project.
  • Modules: A set of Python modules 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.

Acknowledgements

This work is funded by FCT, through the project UIDB/04524/2020.