Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Approaches to Efficient and Robust Cryptographic Protocols

Bartosz Przydatek

Published Jan 1, 2007• 0 citations
Publisher
DOI
10.3929/ethz-a-005392962

Related papers

A study on security and efficiency of cryptographic protocols

春植 朴

•Jan 1, 1995•0 citations

Efficient Cryptographic Protocols Based on Noisy Channels

Claude Crépeau

Lecture notes in computer science•Jan 1, 1997•169 citations

Modular Design of Secure yet Practical Cryptographic Protocols

Ronald Cramer

•Jan 31, 1997•168 citations

On the performance of quantum cryptographic protocols SARG04 and KMB09

Minal Lopes, Nisha Sarwade

Since the first protocol, proposed by Benette and Brassard in 1984 (BB84), the Quantum Cryptographic(QC) protocols has been studied widely in recent years. It is observed that most of the later QC protocols are variants of BB84 with the intention of addressing one or more problems incurred during the practical implementation of BB84 protocol. Amongst many candidates, SARG04 provides robust performance for the weak coherent pulse implementation of QC. Another follower protocol, KMB09 provides improvement in quantum communication distance between Alice and Bob (the classical communicating parties). Both these protocols are chosen to compete, as they found to be a suitable choice for incorporating QC in existing wireless technology. In this paper we present the performance analysis of these two protocols with respect to protocol efficiency, Quantum Bit Error Rate (QBER) and robustness against eavesdropping.

International Conference on Communication, Information & Computing Technology•Jan 1, 2015•17 citations

Leveraging Machine Learning for Performance Optimization in Post-Quantum Cryptographic Protocols

John Olusegun Fajinmi

The advent of quantum computing presents a potential threat to conventional cryptographic systems, prompting the urgent need for post-quantum cryptography (PQC) solutions. However, the complexity of these cryptographic protocols often leads to inefficiencies in performance, hindering their widespread adoption. This paper explores the integration of machine learning (ML) techniques to optimize the performance of post-quantum cryptographic protocols. We propose an innovative framework where ML algorithms are leveraged to enhance the efficiency of key generation, encryption, decryption, and signature processes in PQC schemes. By utilizing data-driven approaches such as reinforcement learning and neural networks, we aim to reduce computational overhead and improve latency, while maintaining the robustness and security of post-quantum algorithms. Experimental results demonstrate significant performance improvements over traditional optimization methods, showcasing the potential of ML in advancing the practicality of PQC protocols. This work lays the foundation for a new class of cryptographic systems capable of withstanding quantum threats while maintaining high operational efficiency.

•Apr 14, 2025•0 citations

Modular design of practical and provably secure cryptographic protocols

Ronald Cramer

•Jan 1, 1997•0 citations
Since the first protocol, proposed by Benette and Brassard in 1984 (BB84), the Quantum Cryptographic(QC) protocols has been studied widely in recent years. It is observed that most of the later QC protocols are variants of BB84 with the intention of addressing one or more problems incurred during the practical implementation of BB84 protocol. Amongst many candidates, SARG04 provides robust performance for the weak coherent pulse implementation of QC. Another follower protocol, KMB09 provides improvement in quantum communication distance between Alice and Bob (the classical communicating parties). Both these protocols are chosen to compete, as they found to be a suitable choice for incorporating QC in existing wireless technology. In this paper we present the performance analysis of these two protocols with respect to protocol efficiency, Quantum Bit Error Rate (QBER) and robustness against eavesdropping.
The advent of quantum computing presents a potential threat to conventional cryptographic systems, prompting the urgent need for post-quantum cryptography (PQC) solutions. However, the complexity of these cryptographic protocols often leads to inefficiencies in performance, hindering their widespread adoption. This paper explores the integration of machine learning (ML) techniques to optimize the performance of post-quantum cryptographic protocols. We propose an innovative framework where ML algorithms are leveraged to enhance the efficiency of key generation, encryption, decryption, and signature processes in PQC schemes. By utilizing data-driven approaches such as reinforcement learning and neural networks, we aim to reduce computational overhead and improve latency, while maintaining the robustness and security of post-quantum algorithms. Experimental results demonstrate significant performance improvements over traditional optimization methods, showcasing the potential of ML in advancing the practicality of PQC protocols. This work lays the foundation for a new class of cryptographic systems capable of withstanding quantum threats while maintaining high operational efficiency.