Privacy Enhancing Technologies

Fall 2024
Instructor: Prof. Florian Tramèr
Contact: florian.tramer@inf.ethz.ch
Lectures: Monday 10:15-12:00, HG E 7
Exercise Sessions: Tuesday 17:15-18:00, CAB G 51 & CAB H 52, and Thursday 17:15-18:00, CAB H 52 & NO C 44
Course catalog


Moodle   –   Gradescope   –   Schedule & notes   –   Homework assignments


Description

Privacy is a fundamental human right! And yet, technological advances (in particular in computer science) can often undermine privacy. In this class we will see how to formalize various notions of privacy and how to build systems that preserve privacy, by combining techniques from cryptography and statistics. The later parts of the course will focus on applications to machine learning.

Assignments

You must use LaTeX to write up your problem set. You must use the provided template to typset your assignment (the provided preamble can be handy for common notations).

You must submit your problem set via Gradescope. Please use the course code provided on Moodle to sign up. Note that Gradescope requires that the solution to each problem starts on a new page.

Please read the collaboration policy!You may discuss the problem sets with other students and you may work together to come up with solutions to the problems. If you do so, you must list the name of your collaborators on the first page of your submission. Each student must write up their problem set independently.

  • Problem Set 1 (source, preamble) – due Friday, Oct 18 (out: Sep 23)
  • Problem Set 2 – due Friday, Nov 8 (out: Oct 21)
  • Problem Set 3 – due Friday, Nov 29 (out: Nov 11)
  • Problem Set 4 – due Friday, Dec 20 (out: Dec 2)

Schedule

DateTopicOptional Readings
Mo, Sep 23

Lecture Notes, Class Notes

  • Logistics
  • Commitment schemes
Mo, Sep 30

Lecture Notes, Class Notes

  • Secret Sharing
  • Secure Multiparty Computation
Mo, Oct 7

Lecture Notes, Class Notes

  • Zero-Knowledge Proofs
  • Non-interactive and succinct proofs
Mo, Oct 14

Lecture Notes, Class Notes

  • Polynomial IOPs
  • The PLONK SNARG
Fr, Oct 18

Problem Set 1 Due at 11:59pm via Gradescope

Mo, Oct 21

Lecture Notes, Class Notes

  • Private Information Retrieval
Mo, Oct 28

Lecture Notes, Class Notes

  • Oblivious RAM
Mo, Nov 4

Lecture Notes, Class Notes

  • Computing Private Statistics
  • Linear PCPs
Fr, Nov 8

Problem Set 2 Due at 11:59pm via Gradescope

Mo, Nov 11

Lecture Notes, Class Notes

  • Data Privacy
  • Data Reconstruction Attacks
Mo, Nov 18

Lecture Notes, Class Notes

  • Randomized Response
  • Differential Privacy
Mo, Nov 25

Lecture Notes, Class Notes

  • Laplace Mechanism
  • Approximate Differential Privacy
Fr, Nov 29

Problem Set 3 Due at 11:59pm via Gradescope

Mo, Dec 2

Lecture Notes, Class Notes

  • Private learning
  • DP-SGD
Mo, Dec 9

Lecture Notes, Class Notes

  • Membership Inference Attacks
  • Privacy Auditing
Mo, Dec 16

Lecture Notes, Class Notes

  • Privacy across the Machine Learning pipeline
  • Federated Learning
Fr, Dec 20

Problem Set 4 Due at 11:59pm via Gradescope


Feedback

This is the first time we teach this class, so we would love to get feedback on how to improve it! You can use this form to give us anonymous feedback throughout the semester. You can fill it out as often as you want. And we make mistakes! If something looks wrong or impossible, please let us know.

Course Staff