This is the course space for the Aalto University Department of Computer Science Research seminar on security and privacy of machine learning (CS-E4001). The course is worth 5 credits, which are earned
by reading, analyzing, presenting and discussing research papers on the topic of security and privacy of machine learning systems. There is no exam.
Course staff: Samuel Marchal (responsible teacher - email@example.com), Buse Gul Atli (co-organizer - firstname.lastname@example.org), Sebastian Szyller (co-organizer - email@example.com).
Students must register for
the course through Oodi by March 2, 2021. We expect 10-15 participants for this course.
Zoom Link & Passcode, Teams
All meetings will be hosted on Zoom. Use the following link: https://aalto.zoom.us/j/61731561475?pwd=cDdMN3FqdkNTc1ZpRUhXSHMvMVlFdz09
All course related discussion will happen on Microsoft Teams. Follow the link below to join the workspace: Microsoft Teams link
After this course, you are expected to have the following new skills:
- knowledge of the security and privacy threats to machine learning systems
- ability to identify the threats to a given machine learning system (threat modelling)
- ability to summarize and critically analyze findings/contributions from research papers
- ability to make a sensible oral presentation of a research paper and to lead a critical discussion about it
- new insights on good research methodology and on scientific writing (useful for MSc. thesis)
The course consists of several group discussion sessions (9 sessions planned). Two scientific papers on the topic of security and privacy of machine learning are presented and discussed during each session. These papers cover both attacks on machine learning systems and defenses to some of these attacks. One student presents and leads the discussion for each paper. The remaining of the students participate in the discussions.
Each paper discussion will typically consist in the presentation of the paper (20 minutes) and an interactive discussion led by the presenter (30 minutes).
A small programing assignment introduces how to craft adversarial examples in order to perform evasion attacks
Assessment and grading
Students are assessed and graded according to 4 components:
- Presenting and leading the discussion on a scientific paper (twice per student): 50% of the grade
- Participation in discussions: 15% of the grade
- Writing paper takeaways and questions: 15% of the grade
- Programing assignment: 20% of the grade
Workload: 135 hours
- reading research papers (2 papers per week) - 20 papers x 3h = 60h
- participate to contact sessions (once a week) - 11 sessions x 2h = 22h
- prepare the presentation and the discussion for 1 paper (twice over the course) - 2 preparations x 10h = 20h
- write paper takeaways and questions (1 page to fill once a week, before each discussion session) - 9 sessions x 1h = 9h
- a programing assignment to implement an evasion attack (generate adversarial examples) - 24h
|Tuesday, March 2
||Introductory lecture: Course organization and topics overview
|Friday, March 12
||Zoom||Info session for programming assignment
|Friday, March 19
||Zoom||Discussion 1: Model evasion
|Friday, March 26
||Zoom||Discussion 2: Model poisoning
|Friday, April 9
||Zoom||Discussion 3: Compromised training library/platform
|Tuesday, April 20
||Zoom||General feedback on presentations + discussions already done
|Friday, April 23
||Discussion 4: Model stealing
|Friday, April 30
||Zoom||Discussion 5: Protecting Intellectual property of models
|Friday, May 7
||Discussion 6: Training data leakage + Tracing training data
|Friday, May 21
||Discussion 7: Privacy-preserving training + Fairness & bias in ML prediction
What students liked about the course last year (selected feedback)
- "It provides a good chance for me to read state-of-the-art articles, the presentation and discussion part also encourage me to think deeper and learn more actively."
- "The lecturers also helped the leader for discussion and opened new topics to discuss. They also assured that we can actually ask something that we are not sure and it is perfectly fine."
- "The discussions, research paper selection, presentations."
- "Experienced participants in the discussions who could contribute interesting points."
- "Really good feedback after our presentations."
- "Really constructive and detailed feedback on how to improve both content and communication."
- "Good feedback!"