Privaatsust säilitavad tehnoloogiad 2021/22 kevad

Lectures and practice sessions

Compared to the past two years, the schedule of the course has been thoroughly reworked. The content has also been somewhat reworked.

The information in this page gives the materials for imminent and past lectures and practice sessions in this year, as well as references to similar lectures in past years. This information includes the lecture slides, as well as instructions to download and/or install something in your computers.

• Week 1 (09.02.2022, Dan Bogdanov):
  • Lecture: Course introduction. Introduction to privacy, data protection and Privacy Enhancing Technologies. Analysis of existing systems.
  • Seminar: Analysis of the intended and emergent effects of new services.
  • Homework: Analysis of the intended and emergent effects of new services.
• Week 2 (16.02.2022, Triin Siil):
  • Lecture 2: Legal status of privacy technologies.
• Week 3 (23.02.2022): no meeting, due to the upcoming National Holiday.
• Week 4 (02.03.2022, Kristjan Krips):
  • Lecture notes: Attacks on privacy
  • Slides
  • Lab tasks
  • Recording of the lecture
  • Recording of the lab
  • OSINT homework
• Week 5 (09.03.2022, Raimundas Matulevičius): Designing privacy-preserving systems (part 1), focusing on GDPR.
  • Lecture slides and recording
  • homework tasks linked from week 6
• Week 6 (16.03.2022, Raimundas Matulevičius): Designing privacy-preserving systems (part 2), focusing on modelling of business processes using PETs.
  • Lecture slides, recording, and used models.
  • Homework tasks
• Week 7 (23.03.2022, Liina Kamm): Introduction to data subject privacy enhancing techniques. Pseudonymisation. Anonymisation
  • Lecture slides
  • Recording of the lecture
• Week 8 (30.03.2022, Dan Bogdanov): Privacy-preserving computations. We will use Sharemind as technology for instruction.
  • Lecture: Privacy-preserving computation
  • Lab: Converting algorithms to a privacy-preserving form with Sharemind SDK
  • Homework: Side-channel safe programming for MPC.
• Week 9 (06.04.2022, Alisa Pankova): Differential privacy.
  • Lecture slides
  • Exercise sheet
    • dpLab_init.py
    • dpLab_data.csv
    • dpLab_final.py
  • Homework: Differential privacy.
  • Links to additional information:
    • Mathematical and technical background:
      • A short summary of SQL commands (SQL is used in the slides and in the exercises to describe database queries): https://www.cs.utexas.edu/~mitra/csFall2006/cs329/lectures/sql.html.
      • Probability density functions: https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library/random-variables-continuous/v/probability-density-functions
    • Real-world applications of differential privacy
      • A brief overview of DP and some real-world use cases: https://research.aimultiple.com/differential-privacy/
      • More real-world use cases: https://desfontain.es/privacy/real-world-differential-privacy.html.
      • Impacts on the usefulness of the result (the example of US census data): https://eu.azcentral.com/story/news/local/arizona/2021/08/10/2020-census-data-differential-privacy/5541578001/
• Week 10 (13.04.2022, Liina Kamm): Privacy-Preserving Distributed Statistics and Machine Learning. Data synthesis.
  • Lecture slides
  • Recording of the lecture
• Week 11 (20.04.2022, Jan Willemson): Introduction to cryptography. Slides for lectures 11-13.
• Week 12 (27.04.2022, Jan Willemson): Public key cryptography, ElGamal, RSA, Diffie-Hellman key exchange, blind signatures.
• Week 13 (04.05.2022, Jan Willemson): Ring signatures, zero-knowledge proofs.
• Week 14 (11.05.2022, Pille Pullonen-Raudvere): privacy-preserving protocols for specific and general computational tasks. Slides are here and presentation video is in BBB.
• Week 15 (18.05.2022, Peeter Laud): Anonymous communication.
  • Homework(s) will be given sometime over weeks 11-15.
• Week 16 (25.05.2022, TBA, possibly several people): Concluding seminars, and problem-solving. May lead to, or at least significantly affect the final grade for the course.