Katharina Boudgoust

Postdoctoral Researcher


Themenschwerpunkt: Lattice-Based Cryptography

Twittername: @KBoudgoust

Webseiten/Blogs: https://katinkabou.github.io/

Sprache/n: Englisch, Französisch, Deutsch

Stadt: Aarhus

Land: Dänemark

Ich biete: Vortrag, Seminarleitung

  Ich bin bereit, für eine Veranstaltung zu reisen.

  Ich bin bereit, für gemeinnützige Zwecke kostenfrei zu sprechen.

Biografie:

Since January 2022, I am a postdoc working on lattice-based cryptography and part of the Cryptography and Security Team of the Aarhus University in Denmark.

From 2018 to 2021, I was a PhD student within the EMSEC team at the IRISA Laboratory in Rennes under the supervision of Adeline Roux-Langlois and Pierre-Alain Fouque.

From October to December 2019, I visited Ron Steinfeld at the Cybersecurity Lab of the Faculty of Information Technology of the Monash University in Melbourne, Australia.

In Mai 2018, I received my master's degree at the Department of Mathematics at Karlsruhe Institute of Technology.

Vorträge / Referenzen:

Middle-Product Learning with Rounding Problem and its Applications

This talk focuses on a new variant of the Learning With Errors (LWE) problem, a fundamental computational problem used for lattice-based cryptography.
At Crypto17, Roşca et al. introduced the Middle-Product LWE problem (MP-LWE), whose hardness is based on the hardness of the Polynomial LWE (P-LWE) problem parameterized by a set of polynomials, making it more secure against the possible weakness of a single defining polynomial. As a cryptographic application, they also provided an encryption scheme based on the MP-LWE problem. In this talk, I present a deterministic variant of their encryption scheme, which does not need Gaussian sampling and is thus simpler than the original one. Still, it has the same quasi-optimal asymptotic key and ciphertext sizes. The hardness of the scheme is based on a new assumption called Middle-Product Computational Learning With Rounding. We prove that this new assumption is as hard as the decisional version of MP-LWE and thus benefits from worst-case to average-case hardness guarantees.
This is a joint work with Shi Bai, Dipayan Das, Adeline Roux-Langlois, Weiqiang Wen and Zhenfei Zhang.

Dieser Vortrag ist auf: Englisch