After successful completion of the course, students are able to...

- Identify threats to privacy of individuals in machine learning datasets

- Select fitting solutions for privacy-preserving machine learning

- Understand attack vectors on machine learning models, and how attacls can be detected and mitigated

- Select fitting concepts for explainable and interpretable machine learning

• Privacy-preserving techniques to anonymize sensitive information in the input data, e.g. to facilitate data sharing, with a specific focus on the implications on the utility of the data and the models trained thereon. This includes e.g. k-anonymity and related models such as l-diversity, as well as differential privacy, etc.
• Privacy-preserving techniques, such as differential privacy, to prevent information leaks from trained models
• Attack vectors on machine learning models, e.g. membership attacks, and model stealing, Adversary Input Generation and how to limit them
• Backdoor embedding to manipulate the behaviour of seemingly benign models for malicious purposes
• Privacy-preserving computation of machine learning models, e.g. with secure multi-party computation, and homomorphic encryption approaches
• Explainability of machine learning models to facilitate a better understanding and trust in the models, e.g. via visualization, rule extraction, Zero-Shot Learning

The course contains classroom lectures and exercises. Exercises include the application of privacy-preserving, secure and explainabel machine learning techniques for various data sets and implementation of thses techniques. The exercises are prepared at home and will be presented/discussed during the exercise classes.  

Dates:

5.3. 2020: Preliminary talk (Vorbesprechung) & intro

For all other dates, please see TUWEL! Note that the lecture won't take place every week!

- Solving of exercises regarding experiments in secruity, privacy and explainability of machine learning, using a software toolkit of the student's choice (e.g. Python scikit-learn, Matlab, R, WEKA, ...)

- Written exam at the end of the semester

184.702 Machine Learning