Quantum Enhanced Entropy Pool for Cryptographic Applications and Proofs

2024-10-15

2024 IEEE Future Networks World Forum (FNWF)

AI Summary

Traditional verifiable random functions (VRFs) rely on pseudorandom entropy sources, resulting in insufficient entropy quality and weak resistance to prediction attacks. Method: This work pioneers the integration of a quantum random number generator (QRNG) into an Ed25519-based VRF, replacing the standard entropy source in a Go-language cryptographic implementation; security gains and performance overhead are quantified at the API level. Results: QRNG integration substantially improves entropy quality and quantum-resilient unpredictability of VRF keys and outputs. However, it incurs measurable costs: key generation latency increases to over 400 µs (+100%), verification latency rises to 3500 µs (+75%), and CPU utilization reaches 30% (+20 percentage points). This study provides the first empirical benchmark for security–performance trade-offs in post-quantum VRF design and deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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+ µs, verification times from 500 to 3500 µs) and higher CPU usage (17% to 30%) compared to the more consistent performance of a Go-based VRF (key generation times below 200 µs, verification times under 2000 µs, CPU usage below 10%), highlighting trade-offs in computational efficiency and resource demands.

Problem

• Enhancing cryptographic security with quantum randomness in VRFs
• Assessing performance impact of quantum entropy on key metrics
• Balancing unpredictability and efficiency in quantum-enhanced cryptographic systems

Innovation

• Integrates quantum randomness into VRFs
• Uses Ed25519 for cryptographic security
• Simulates QRNG-based VRF setup