Private and Secure Post-Quantum Verifiable Random Function with NIZK Proof and Ring-LWE Encryption in Blockchain

• URL: http://arxiv.org/abs/2311.11734v2
• Date: Wed, 7 Feb 2024 12:39:57 GMT
• Title: Private and Secure Post-Quantum Verifiable Random Function with NIZK Proof and Ring-LWE Encryption in Blockchain
• Authors: Bong Gon Kim, Dennis Wong, Yoon Seok Yang,
• Abstract summary: We present a blockchain-based Verifiable Random Function (VRF) scheme addressing some limitations of classical VRF constructions. To enhance our VRF's secure randomness, we adopt post-quantum Ring-LWE encryption for pseudo-random sequences. Our results exhibit a 98.86% pass rate over 11 test cases, with an average p-value of 0.5459 from 176 total tests.
• Score: 1.4792750204228
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a secure and private blockchain-based Verifiable Random Function (VRF) scheme addressing some limitations of classical VRF constructions. Given the imminent quantum computing adversarial scenario, conventional cryptographic methods face vulnerabilities. To enhance our VRF's secure randomness, we adopt post-quantum Ring-LWE encryption for synthesizing pseudo-random sequences. Considering computational costs and resultant on-chain gas costs, we suggest a bifurcated architecture for VRF design, optimizing interactions between on-chain and off-chain. Our approach employs a secure ring signature supported by NIZK proof and a delegated key generation method, inspired by the Chaum-Pedersen equality proof and the Fiat-Shamir Heuristic. Our VRF scheme integrates multi-party computation (MPC) with blockchain-based decentralized identifiers (DID), ensuring both security and randomness. We elucidate the security and privacy aspects of our VRF scheme, analyzing temporal and spatial complexities. We also approximate the entropy of the VRF scheme and detail its implementation in a Solidity contract. Also, we delineate a method for validating the VRF's proof, matching for the contexts requiring both randomness and verification. Conclusively, using the NIST SP800-22 of the statistical randomness test suite, our results exhibit a 98.86% pass rate over 11 test cases, with an average p-value of 0.5459 from 176 total tests.

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