Work

Deep-learning-based radio-frequency side-channel attack on quantum key distribution

Year: 2023

Type: article

Abstract: Quantum key distribution (QKD) is a technique that allows two distant parties to distribute and share a common secret, which then can be used as a cryptographic key. While mathematical proofs verify the security of perfectly implemented systems, imperfections in real devices allow attackers to retrieve information. This study uses machine-learning techniques to investigate information leakage via radio-frequency emissions of QKD device electronics. The approach allows researchers and engineers to harden devices against attacks.

Source: Physical Review Applied

DOI: 10.1103/physrevapplied.20.054040

Authors: Adomas Baliuka, Markus Stöcker, Michael Auer, Peter Freiwang, Harald Weinfurter, Lukas Knips

Institutions: Munich Center for Quantum Science and Technology, Ludwig-Maximilians-Universität München, University of Gdańsk, Max Planck Institute of Quantum Optics

Language: English

Topic: Quantum Information and Cryptography

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: California Department of Fish and Game, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Elitenetzwerk Bayern

Awards: G1768993395, G4710139657, EXC 2111: Munich Center for Quantum Science and Technology (MCQST)