Privacy Enhancing technology

lelec2770  2020-2021  Louvain-la-Neuve

Privacy Enhancing technology
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 30.0 h
Pereira Olivier (coordinator); Standaert François-Xavier;
Main themes
The exact course topics will change from year to year. Examples of relevant topics include techniques that make it possible to :
  • compute on encrypted data;
  • build a database that can be queried without the server knowing which parts of it are accessed;
  • have anonymous communications;
  • make digital cash;
  • shuffle cards over the internet;
  • organize an election in which the organizers can't cheat;
  • have services with access control that keep users untraceable;
  • understand attacks against privacy, including de-anoymization/re-identification attacks, profiling, data mining and side-channel attacks;
  • identify privacy issues related to mass surveillance and solutions to prevent them.

At the end of this learning unit, the student is able to :

1 Based on the LO referential of the program « Master in Electrical Engineering », this course contributes to the development, acquisition, and evaluation of the following learnging outcomes :
  • AA1.2, AA1.3,
  • AA2.2, AA2.3, AA2.5,
  • AA3.1,
  • AA5.1, AA5.3, AA5.4, AA5.6,
  • AA6.1, AA6.2, AA6.3
Specific learning outcomes of the course
  • At the end of this class, the student will be able to  :
  • Analyze the risks of attacks against correctness and privacy for a concrete system
  • Understand cryptographic and architectural tools allowing to mitigate privacy issues
  • Evaluate utility and privacy metrics for databases and distributed systems
Various themes will be discussed each year.
These themes may include: secure two-party and multi-party protocols, oblivious memories, verifiable voting, crypto-currencies, verifiable computation, anonymous credentials, differential privacy and big data, post-Snowden cryptography.
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Lectures and exercise sessions.
Homeworks and mini-projects may be proposed during the semester.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The final examination is based on exercises, based on the learning outcomes listed above.
One of more mini-projects may be proposed during the semester and contribute to the final grade.
The practical details are given on Moodle.
Teaching materials
  • Slides and online references are available from Moodle.
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Electrical Engineering

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology