UWisconsin CS 763: Security and Privacy in Data Science (Previously CS 839: Topics in Security and Privacy)
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Lectures will be loosely organized around three core modules: differential
privacy, adversarial machine learning, and applied cryptography. We will also
cover two advanced modules: algorithmic fairness, and PL and verification
techniques.
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This is a graduate seminar, so not all lectures are set in stone and there is
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considerable flexibility in the material. If you are interested in something not
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covered in the syllabus, please let me know!
## Course Materials
For differential privacy, we will use the textbook *Algorithmic Foundations of
Data Privacy* (AFDP) by Cynthia Dwork and Aaron Roth, available
[here](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf).
## Grading and Evaluation
Grades will be assigned as follows:
- **Paper presentations: 25%**
- **Homeworks: 15%**
- **Final project: 60%** (Milestones 1 and 2, and final writeup)
These three components are detailed below.
### Paper presentations
**Paper discussions** are one of the main components of this course. Before
every presentation, you are expected to read the paper closely and understand
its significance, including (a) the main problem addressed by the paper, (b) the
primary contributions of the paper, and (c) how the authors solve the problem in
some technical detail. Of course, you are also expected to attend discussions
and actively participate in the discussion.
The topics we will be reading about are from the recent research
literature---peer-reviewed and published, but not completely refined. Most
research papers focus on a very narrow topic and are written for a very specific
technical audience. It also doesn't help that researchers are generally not the
clearest writers, though there are certainly exceptions. These
[notes](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) by
Srinivasan Keshav may help you get more out of reading papers.
To help you prepare for the class discussions, I will also send out a few
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questions at least 24 hours before every paper presentation. **Before** each
lecture, you should send me brief answers---a short email is fine, no more than
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a few sentences per question. These questions will help you check that you have
understood the papers---they are not meant to be very difficult or
time-consuming and they will not be graded in detail.
### Homeworks
After each of the first three core modules, we will assign a small homework
assignment. These assignments are not weighed heavily---though they will be
graded---but they are mostly for you to check that you have grasped the
material.
### Course Project
The main component is the **course project**. You will work individually or in
pairs on a topic of your choice, producing a conference-style write-up and
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presenting the project at the end of the semester. Successful projects may have
the potential to turn into an eventual research paper or survey. Details can be
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found [here](assignments/project.md).
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## Learning Outcomes
By the end of this course, you should be able to...
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- Summarize the basic concepts in differential privacy, applied cryptography,
language-based security, and adversarial machine learning.
- Use techniques from differential privacy to design privacy-preserving data
analyses.
- Grasp the high-level concepts from research literature on the main course
topics.
- Present and lead a discussion on recent research results.
- Carry out an in-depth exploration of one topic in the form of a self-directed
research project.
## Credit Information
This is a **3-credit** graduate seminar. For the first 10 weeks of the fall
semester, we will meet for three 75-minute class periods each week. You should
expect to work on course learning activities for about 3 hours out of classroom
for each hour of class.
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## Academic Integrity
The final project may be done in groups of three (or in rare situations, two)
students. Collaborative projects with people outside the class may be allowed,
but check with me first. Everything else you turn in---from homework assignments
to discussion questions---should be **your own work**. Concretely: you may
discuss together, but **you must write up solutions entirely on your own,
without any records of the discussion (physical, digital, or otherwise)**.
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## Access and Accommodation
The University of Wisconsin-Madison supports the right of all enrolled students
to a full and equal educational opportunity. The Americans with Disabilities Act
(ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document
1071) require that students with disabilities be reasonably accommodated in
instruction and campus life. Reasonable accommodations for students with
disabilities is a shared faculty and student responsibility. Students are
expected to inform me of their need for instructional accommodations by the end
of the third week of the semester, or as soon as possible after a disability has
been incurred or recognized. I will work either directly with you or in
coordination with the McBurney Center to identify and provide reasonable
instructional accommodations. Disability information, including instructional
accommodations as part of a student’s educational record, is confidential and
protected under FERPA.