[Master]Leakage in presence of an active and adaptive adversary

Advisors : Cristian Ene and Laurent Mounier

Measuring the information leakage of a system is very important for security. From side-channels to biases in random number generators, quantifying how much information a system leaks about its secret inputs is crucial for preventing adversaries from exploiting it; this has been the focus of intensive research efforts in the areas of privacy and of quantitative information flow (QIF).

The goal of this internship is :

  • to develop an algorithm able to quantify the information leaked by an application about the secret in a realistic security model that takes into account a very powerful adversary, able to get side-channel informations about the execution of the application (for example, the branchings taken during an execution)
  • to implement this algorithm via abstract interpretation, for example by extending the probabilistic polyhedra model.

More detail is available in the attached pdf description.

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12 December 2022
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