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
Q1
Teacher(s)
Language
English
Prerequisites
Familiarity with the basic notions of cryptography is welcome
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.
Aims

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
 
Content
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
Sigle
Credits
Prerequisites
Aims
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