5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Pereira Olivier coordinator; Standaert François-Xavier;
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 :
Specific learning outcomes of the course
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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.
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
Lectures and exercise sessions.
Evaluation methods
The final examination is based on exercises, based on the learning outcomes listed above. The practical details are given on Moodle.
Online resources
Bibliography
Des transparents et des références sont disponibles sur le site Moodle du cours.
Faculty or entity
ELEC
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Computer Science and Engineering
Master [120] in Electrical Engineering
Master [120] in Computer Science
Master [120] in Mathematical Engineering
Master [120] in data Science: Information technology