T
Thomas Espitau
Researcher at Pierre-and-Marie-Curie University
Publications - 43
Citations - 656
Thomas Espitau is an academic researcher from Pierre-and-Marie-Curie University. The author has contributed to research in topics: Probabilistic logic & Cryptography. The author has an hindex of 12, co-authored 42 publications receiving 450 citations. Previous affiliations of Thomas Espitau include Centre national de la recherche scientifique & University of Paris.
Papers
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Journal ArticleDOI
Proving Expected Sensitivity of Probabilistic Programs
TL;DR: In this paper, the authors propose an average notion of program sensitivity for probabilistic programs, called expected sensitivity, that averages a distance function over a probability distribution of two output distributions from two similar inputs.
Book ChapterDOI
On a dual/hybrid approach to small secret LWE
TL;DR: In this paper, the authors investigated the security of the learning with error (LWE) problem with small secrets by refining and improving the dual lattice attack on a projected sublattice, which allows generating instances of the LWE problem with a slightly bigger noise that correspond to a fraction of the secret key.
Proceedings Article
Proving expected sensitivity of probabilistic programs
TL;DR: In this article, an average notion of program sensitivity for probabilistic programs, expected sensitivity, is proposed, which is based on Lipschitz continuity, which describes how small changes in a program's input lead to bounded changes in the output.
Book ChapterDOI
An Assertion-Based Program Logic for Probabilistic Programs
TL;DR: The Ellora program logic as mentioned in this paper is a sound and relatively complete assertion-based program logic, and demonstrate its expressivity by verifying several classical examples of randomized algorithms using an implementation in the EasyCrypt proof assistant.
Journal ArticleDOI
Proving expected sensitivity of probabilistic programs
TL;DR: In this paper, the authors propose an average notion of program sensitivity for probabilistic programs, called expected sensitivity, that averages a distance function over a probability distribution of two output distributions from two similar inputs.