Administrative information
| Title |
Privacy in Machine Learning |
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| Duration |
90 min |
| Module |
B |
| Lesson Type |
Lecture |
| Focus |
Ethical - Trustworthy AI |
| Topic |
Privacy |
Keywords
Adversary models,Training data extraction,Membership attack,Model extraction,
Learning Goals
- Understanding of privacy risks in machine learning
- Distinguish training data and model extractions attacks/threats
- Learn adversarial modelling and threat analysis in AI
- Learn the principles of AI privacy auditing
- Distinguish membership and reconstruction attacks
- Distinguish membership attack and model inversion
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- basics of machine learning,
- basic linear algebra,
- basic function analysis
Optional for Students
None.
References and background for students:
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
This course provides a general introduction to different confidentiality issues of Machine learning. Teachers are recommended to use real-life examples to demonstrate the practical relevance of these vulnerabilities especially for privacy-related issues whose practical relevance is often debated and considered as an obstacle to human development. Students must understand that privacy risks can also slow down progress (parties facing confidentiality risks may be reluctant to share their data). It focuses on the basic understanding needed to recognize privacy threats for the purpose of auditing machine learning models. Related practical skills can be further developed in more practical learning events:
Outline
| Duration (min) |
Description |
Concepts |
| 20 |
Machine Learning: Recap |
Learning algorithm, Classification, Neural networks, Gradient descent, confidence scores |
| 5 |
Adversary models |
White-box, Black-box attacks |
| 20 |
Membership attack |
Target model, Attacker model, Differential Privacy |
| 20 |
Modell inversion |
Gradient descent with respect to input data, reconstruction of class average |
| 20 |
Model extraction |
Re-training, parameter reconstruction, mitigations |
| 5 |
Conclusions |
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More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
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The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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