A Comparative Analysis of zk-SNARKs and zk-STARKs: Theory and Practice

TL;DR

This paper systematically compares zk-SNARKs and zk-STARKs in real-world conditions, analyzing their performance trade-offs, implementation details, and suitability for various privacy-preserving applications.

Contribution

It provides the first empirical, implementation-level comparison of zk-SNARKs and zk-STARKs on consumer hardware, highlighting practical performance differences and bottlenecks.

Findings

zk-SNARKs generate proofs 68x faster with 123x smaller size

zk-STARKs verify faster and are transparent and post-quantum secure

Distinct computational bottlenecks identified in profiling

Abstract

Zero-knowledge proofs (ZKPs) are central to secure and privacy-preserving computation, with zk-SNARKs and zk-STARKs emerging as leading frameworks offering distinct trade-offs in efficiency, scalability, and trust assumptions. While their theoretical foundations are well studied, practical performance under real-world conditions remains less understood. In this work, we present a systematic, implementation-level comparison of zk-SNARKs (Groth16) and zk-STARKs using publicly available reference implementations on a consumer-grade ARM platform. Our empirical evaluation covers proof generation time, verification latency, proof size, and CPU profiling. Results show that zk-SNARKs generate proofs 68x faster with 123x smaller proof size, but verify slower and require trusted setup, whereas zk-STARKs, despite larger proofs and slower generation, verify faster and remain transparent and post-quantum secure. Profiling further identifies distinct computational bottlenecks across the two systems, underscoring how execution models and implementation details significantly affect real-world performance. These findings provide actionable insights for developers, protocol designers, and researchers in selecting and optimizing proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups.