1. Ancient cryptography: Vigenère, Enigma, Vernam cipher, Shannon theory
  2. Diffie-Hellman cryptography: algebra, Diffie-Hellman, ElGamal
  3. RSA cryptography: number theory, RSA, factoring
  4. Elliptic curve cryptography: elliptic curves over a finite field, ECDH, ECIES
  5. Symmetric encryption: block ciphers, stream ciphers, exhaustive search
  6. Integrity and authentication: hashing, MAC, birthday paradox
  7. Applications to symmetric cryptography: mobile telephony, Bluetooth, WiFi
  8. Public-key cryptography: cryptosystem, digital signature
  9. Trust establishment: secure communication, trust setups
  10. Case studies: Bluetooth, TLS, SSH, PGP, biometric passport

Zoom link:

Meeting ID: 962 8707 8331

This course will provide a broad overview of information security and privacy topics, with the primary goal of giving students the knowledge and tools they will need "in the field" in order to deal with the security/privacy challenges they are likely to encounter in today's "Big Data" world.

In this course you will learn and understand the main ideas that underlie and the way networks are built and run. You will be able to apply the concepts to the smart grid. In the labs you will exercise practical configurations. You will be able to

  • Test and clarify your understanding of the networking concepts by connecting computers to form a LAN, interconnected by routers and interconnected autonomous routing domains. 
  • test traffic control settings
  • develop and test various communicating programs using sockets
  • be familiar with IPv6 as well as IPv4 and the interworking between them
  • run a virtual networking environment in your computer where and deploy real networks in an emulated environment

This is a master level course for master and PhD students

In this course, various aspects of probability theory are considered. The first part is devoted to the main theorems in the field (law of large numbers, central limit theorem), while the second part focuses on the theory of martingales in discrete time.

The goal of this class is to present signal processing tools from an intuitive geometric point of view which is at the heart of all modern signal processing techniques from Fourier transforms and sampling theorems to time-frequency analysis and wavelets. The course is designed to provide the mathematical depth and rigor needed for the study of advanced topics in signal processing and also features introductions to current applications where such tools are crucial. In particular, several applications will be studied, including image compression with linear and non-linear approximation, array signal processing , mobile sensing, and prediction of the stock market. 

During this course, students will:
- Master the right tools to tackle advanced signal and data processing problems
- Have an intuitive understanding of signal processing through a geometrical approach
- Get to know the applications that are of interest today
- Learn about topics that are at the forefront of signal processing research

This course is divided into two parts:

  • In the first part, we review important concepts about Markov chains: irreducibility, aperiodicity, recurrence, limiting and stationary distribution, the ergodic theorem, convergence speed towards equilibirum, spectral gap, cutoff phenomenon.

  • In the second part, we apply these concept to sampling, and more precisely to Markov Chain Monte-Carlo (MCMC) sampling, exploring various applications (function minimization, coloring problem, Ising model). In the last part of the course, we also look at exact simulation.