🤖 AI Summary
Existing privacy-preserving technologies struggle to achieve widespread adoption on mobile devices due to their complexity, particularly lacking zero-knowledge proof schemes that support offline verifiability. This work proposes zQR, a novel framework that integrates zkSNARKs with QR codes to enable offline verification on mobile platforms for the first time. To ensure auditability and non-repudiation, verification logs are immutably recorded on a blockchain, while large language models are leveraged to automatically generate verification circuits. Experimental results demonstrate that zQR efficiently supports both proof generation and verification within QR code version 19 at low error correction levels, achieving low latency and manageable on-chain gas costs. These findings confirm the practical feasibility of zQR as a deployable privacy-preserving solution for mobile environments.
📝 Abstract
Privacy is one of the fundamental rights of individuals in modern societies. Yet, the practical adoption of privacy-preserving technologies in daily interactions remains limited. Zero-knowledge proofs offer strong privacy guarantees but are often hindered by their technical complexity. In this paper, we advance the idea of verifiable QR codes that enable off-line verifiers to verify proofs encoded in QR codes. Based on this core idea, we build a novel QR-driven zkSNARK proof verification framework (i.e., zQR) for mobile platforms. The framework integrates blockchain for auditability, non-repudiation and logging; and large-language models for automatic circuit generation. We perform a security discussion of the framework by considering multiple attack surfaces. Furthermore, we present an experimental evaluation measuring temporal costs (proof generation and verification latency, QR code encoding and decoding latency) and financial costs (blockchain gas consumption). Our results demonstrate the feasibility of zQR as a proof-of-concept framework for privacy-preserving verification on mobile platform where proofs are compactly represented with QR code symbol version of 19 with low error correction level. Finally, we discuss potential applications, current limitations and future directions for the broader adoption of privacy-preserving technologies in daily interactions.