Main focus: Lattice-Based Cryptography
Twitter handle: @KBoudgoust
Languages: English, French, German
Topics: cryptography, lattice-based cryptography, number theory, post-quantum cryptography
Willing to travel for an event.
Willing to talk for nonprofit.
Since September 2018, I am a Ph.D student in computer science within the EMSEC team at IRISA Laboratory in Rennes under the supervision of Pierre-Alain FOUQUE and Adeline ROUX-LANGLOIS. I am working on lattice-based cryptography.
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. My master thesis Polycylic groups and applications in cryptography was under the supervision of Frank HERRLICH and Joern MUELLER-QUADE.
Prior to this, I received in July 2014 my bacherlor's degree at the Department of Mathematics and Computer Science at Heidelberg University. My bachelor thesis Die Kompatibilitaet von Determinantenfunktoren mit Spektralsequenzen und der Kohomologie was under the supervision of Otmar VENJAKOB.
Examples of previous talks / appearances:
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.