Szczegóły publikacji
Opis bibliograficzny
Enhancing security of error correction in quantum key distribution using tree parity machine update rule randomization / Bartłomiej GDOWSKI, Miralem Mehic, Marcin NIEMIEC // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2025 — vol. 15 iss. 14 art. no. 7958, s. 1-22. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 21-22, Abstr. — Publikacja dostępna online od: 2025-07-17
Autorzy (3)
- AGHGdowski Bartłomiej
- Mehic Miralem
- AGHNiemiec Marcin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 161406 |
|---|---|
| Data dodania do BaDAP | 2025-07-30 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app15147958 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker’s synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD’s robustness by introducing adaptive neural-based error correction mechanisms.
