zkVC: Fast Zero-Knowledge Proof for Private and Verifiable Computing

2025-04-16

AI Summary

In cloud environments, server-computed results are vulnerable to tampering, while existing zkSNARKs incur high constraint counts and substantial proof-generation overhead for fundamental operations such as matrix multiplication. Method: This paper proposes zkVC, a zero-knowledge verifiable computation framework that introduces Constraint-Reduced Polynomial Circuits (CRPC) and Prefix-Sum Queries (PSQ) — the first of their kind — to drastically compress R1CS size and accelerate verification. zkVC integrates polynomial commitments with circuit-level optimizations to enable private, verifiable computation while preserving both algorithmic and data privacy. Contribution/Results: Experiments demonstrate over 12× speedup in zero-knowledge proof generation for matrix multiplication, enabling millisecond-scale proof generation on standard servers and sub-millisecond client-side verification. The framework is open-sourced.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the computation, data and results are prone to tampering due to the vulnerabilities on the server side. Thus, verifying the integrity of computation is important in the client-server setting. The cryptographic method known as Zero-Knowledge Proof (ZKP) is renowned for facilitating private and verifiable computing. ZKP allows the client to validate that the results from the server are computed correctly without violating the privacy of the server's intellectual property. Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zkSNARKs), in particular, has been widely applied in various applications like blockchain and verifiable machine learning. Despite their popularity, existing zkSNARKs approaches remain highly computationally intensive. For instance, even basic operations like matrix multiplication require an extensive number of constraints, resulting in significant overhead. In addressing this challenge, we introduce extit{zkVC}, which optimizes the ZKP computation for matrix multiplication, enabling rapid proof generation on the server side and efficient verification on the client side. zkVC integrates optimized ZKP modules, such as Constraint-reduced Polynomial Circuit (CRPC) and Prefix-Sum Query (PSQ), collectively yielding a more than 12-fold increase in proof speed over prior methods. The code is available at https://github.com/UCF-Lou-Lab-PET/zkformer

Problem

• Ensures private and verifiable cloud computing with ZKP
• Reduces high computational overhead in zkSNARKs proofs
• Optimizes matrix multiplication for faster ZKP verification

Innovation

• Optimizes ZKP for matrix multiplication
• Integrates CRPC and PSQ modules
• Achieves 12x faster proof speed