Privacy Preserving Data Science

Course Material for the Tutorial on Privacy Enhancing Technologies and PPML

In this tutorial we will go through a series of examples demonstrating the main features of the so-called Privacy Preserving technologies.

In the first part, we will focus on Data Anonymisation and methods for privacy protection. We will see examples of De-Identification, K-Anonymity, and a very short intro to Differential Privacy.

In the second part, we will be shifting the focus more on Machine (Deep) Learning, investigating in more details how Convolutional Networks work on Images (using PyTorch), and how susceptible these methods are to Adversarial Attacks. The FGSM and the Adversarial-Patches attacks will be presented (with examples in PyTorch), along with some considerations on possible counter-meausures.

Finally, in the third part, we will introduce some of the main concepts for Privacy-Preserving Machine Learning, like Federated Learning and Homomorphic Encryption. Two examples on medical datasets will be presented.

Note

All references and further suggested readings are reported in each notebook.

Get the material

Clone the current repository, in order to get the course materials. To do so, once connected to your remote machine (via SSH), execute the following instructions:

cd $HOME  # This will make sure you'll be in your HOME folder
git clone https://github.com/WebValley2021ReImagined/privacy-preserving-data-science.git

Note: This will create a new folder named privacy-preserving-data-science. Move into this folder by typing:

cd privacy-preserving-data-science

Well done! Now you should do be in the right location. Bear with me another few seconds, following instructions reported below 🙏

Updating your Environment

To execute the notebooks in this repository, a few extra packages are required, but installing them in your Conda environment is super easy.

While into the privacy-preserving-data-science.git folder, execute the two following instructions:

conda activate py38_default  #activate your conda environment
pip install -r requirements.txt  # to install the extra packages

🎉 You should be now ready to go!

The last bit is to run your jupyter lab server, and open the notebooks:

jupyter lab

Colophon

Author: Valerio Maggio (@leriomaggio), Senior Research Associate, University of Bristol.

All the Code material is distributed under the terms of the GNU GPLv3 License. See LICENSE file for additional details.

All the instructional materials in this repository is free to use, and made available under the [Creative Commons Attribution license][https://creativecommons.org/licenses/by/4.0/]. The following is a human-readable summary of (and not a substitute for) the full legal text of the CC BY 4.0 license.

You are free:

• to Share---copy and redistribute the material in any medium or format
• to Adapt---remix, transform, and build upon the material

for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

• Attribution---You must give appropriate credit (mentioning that your work is derived from work that is Copyright © Software Carpentry and, where practical, linking to http://software-carpentry.org/), provide a [link to the license][cc-by-human], and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

No additional restrictions---You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Acknowledgment and funding

The material developed in this course has been supported by the JGI seed corn funding call 2020-2021

Contacts

For any questions or doubts, feel free to open an issue in the repository, or drop me an email @ valerio.maggio_at_bristol.ac.uk