194.055 Security, Privacy and Explainability in Machine Learning
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021S, VU, 2.0h, 3.0EC
TUWEL

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

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

Subject of course

  • 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

 

Teaching methods

The course consists of lectures and exercises. Lectures will be live-streamed via Zoom as long as the current restrictions on presence-teaching persist. Links to the Zoom sessions are provided in TUWEL.  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.  

Mode of examination

Immanent

Additional information

The SPEML Lecture will be held on-line!

Note: The lecture has to be held on-line for the time being. The lectures will be streamed live via a Zoom Session, the link and access information will be posted in the TUWEL course. There will be no recorded videos of the lectures. The time slots for the lectures remain unchanged as announced in the lecture room reservation. It is currently not permitted to attend any lectures physically. If the TU regulations should change and physical presence should be come possible we will announce this.

 

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Thu13:00 - 15:0004.03.2021 - 24.06.2021FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Security, Privacy and Explainability in Machine Learning - Single appointments
DayDateTimeLocationDescription
Thu04.03.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu11.03.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu18.03.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu25.03.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu15.04.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu22.04.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu29.04.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu06.05.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu20.05.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu27.05.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu10.06.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu17.06.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen
Thu24.06.202113:00 - 15:00FAV Hörsaal 1 - INF Sicherheit, Privacy und Erklärbarkeit in Maschinellem Lernen

Examination modalities

- 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 on-line via TUWEL. If the pandemic situation allows face-to-face exams at the scheduled time, we would switch to a face-to-face exam. 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).

Course registration

Begin End Deregistration end
21.01.2021 00:00 31.03.2021 23:59 31.03.2021 23:59

Curricula

Literature

No lecture notes are available.

Previous knowledge

184.702 Machine Learning

Preceding courses

Language

English