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.
• Generation of synthetic data as privacy-preserving method
• 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 consists of lectures and exercises. Lectures will be held in-class.  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.  

The SPEML Lecture will be held in class!

Preliminary talk (Vorbesprechung) & Intro: 2.3. 2023, in-class

- 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 (closed book) - most likely in-class, but via TUWEL. In case of low enrollment, the exam can also be conducted orally (also, depending on the development of the pandemic situation, most likely on-line).

184.702 Machine Learning, or a similar Machine Learning lecture