Back to selection
67924-01 - Lecture: Privacy-Preserving Methods for Data Science and Distributed Systems (6 CP)
| Semester | spring semester 2026 |
| Further events belonging to these CP |
67924-01 (Lecture) 67924-02 (Practical course) |
| Course frequency | Irregular |
| Lecturers | Isabel Wagner (isabel.wagner@unibas.ch, Assessor) |
| Content | Data and systems based on data are of enormous value in our modern society: medical datasets can help develop new treatments; real-time location data can help optimize traffic flows or contain outbreaks in a pandemic; speech data can improve speech recognition systems for accessibility or convenience. However, data processing may violate the privacy rights of individuals. In this lecture, you will learn how data can be shared and analyzed in a privacy-preserving way. The following topics will be covered: 1. Private data sharing * Anonymization and de-anonymization attacks * k-anonymity * Differential privacy, local differential privacy * Synthetic data generation 2. Private data analysis * Cryptographic approaches: secret sharing, secure multi-party computation * Privacy preserving machine learning (DPSGD, PATE) * Federated learning 3. Censorship resistance 4. Privacy engineering |
| Learning objectives | * Analyze privacy risks in the contexts of distributed systems and data science * Understand the principles behind different privacy-enhancing technologies and how they address the privacy risks * Implement, configure, and evaluate privacy-preserving solutions |
| Bibliography | Will be announced in the lecture. |
| Comments | Note for students of the Master Data Science: you can take this lecture as part of the module "Systems Foundations". |
| Weblink | https://dmi.unibas.ch/de/studium/compute |
| Admission requirements | Foundations of Distributed Systems (offered in the fall semester) is a prerequisite. For students who have not taken it, the lecturer can provide the list of relevant topics for independent study. Knowledge of the following topics is a plus, but not a prerequisite. * Programming in Python Students with reduced computer science background are encouraged to discuss their prior knowledge with the lecturer. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Monday | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
Dates
| Date | Time | Room |
|---|---|---|
| Monday 16.02.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 23.02.2026 | 16.15-18.00 | Fasnachtsferien |
| Monday 02.03.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 09.03.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 16.03.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 23.03.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 30.03.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 06.04.2026 | 16.15-18.00 | Ostern |
| Monday 13.04.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 20.04.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 27.04.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 04.05.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 11.05.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 18.05.2026 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Monday 25.05.2026 | 16.15-18.00 | Pfingstmontag |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Applications of Machine Intelligence (Master's Studies: Computer Science) Module: Concepts of Distributed Systems (Master's degree subject: Computer Science) Module: Methods of Distributed Systems (Master's Studies: Computer Science) Module: Systems Foundations (Master's Studies: Data Science) |
| Assessment format | continuous assessment |
| Assessment details | continuous assessment Please note: 40% project (writeup and presentation) 60% written exam A 50% score on each homework set is required to participate in the final exam. Expected date for written exam: tba for project presentations: tba |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,5 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Informatik |