Quantum Enhanced Entropy Pool for Cryptographic Applications and Proofs

• URL: http://arxiv.org/abs/2506.14340v1
• Date: Tue, 17 Jun 2025 09:25:36 GMT
• Title: Quantum Enhanced Entropy Pool for Cryptographic Applications and Proofs
• Authors: Buniechukwu Njoku, Sonai Biswas, Milad Ghadimi, Mohammad Shojafar, Gabriele Gradoni, Riccardo Bassoli, Frank H. P. Fitzek,
• Abstract summary: This paper investigates the integration of quantum randomness into Verifiable Random Functions (VRFs)<n>We assess the impact on key security and performance metrics, including execution time, and resource usage.<n>The results show that while QRNGs could enhance the unpredictability and verifiability of VRFs, their incorporation introduces challenges related to temporal and computational overhead.
• Score: 9.574949437047984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the integration of quantum randomness into Verifiable Random Functions (VRFs) using the Ed25519 elliptic curve to strengthen cryptographic security. By replacing traditional pseudorandom number generators with quantum entropy sources, we assess the impact on key security and performance metrics, including execution time, and resource usage. Our approach simulates a modified VRF setup where initialization keys are derived from a quantum random number generator source (QRNG). The results show that while QRNGs could enhance the unpredictability and verifiability of VRFs, their incorporation introduces challenges related to temporal and computational overhead. This study provides valuable insights into the trade-offs of leveraging quantum randomness in API-driven cryptographic systems and offers a potential path toward more secure and efficient protocol design. The QRNG-based system shows increased (key generation times from 50 to 400+ microseconds, verification times from 500 to 3500 microseconds) and higher CPU usage (17% to 30%) compared to the more consistent performance of a Go-based VRF (key generation times below 200 microseconds, verification times under 2000 microseconds, CPU usage below 10%), highlighting trade-offs in computational efficiency and resource demands.

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