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

Ayush Nainwal, Atharva Kamble, Nitin Awathare

TL;DR

This work addresses the practical performance trade-offs between Groth16 zk-SNARKs and zk-STARKs on commodity hardware by performing an empirical, implementation-level comparison. It combines theoretical framing with benchmarks on real devices to quantify proof size, generation time, verification time, and resource usage. Key findings show SNARKs deliver much smaller proofs and faster proof generation, while STARKs offer transparent, setup-free, post-quantum security with competitive verification performance depending on implementation. These insights guide developers and designers in selecting and optimizing zero-knowledge proof systems for privacy-preserving transactions, verifiable computation, and scalable rollups, highlighting where each approach excels and where further optimization is needed.

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.

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

TL;DR

This work addresses the practical performance trade-offs between Groth16 zk-SNARKs and zk-STARKs on commodity hardware by performing an empirical, implementation-level comparison. It combines theoretical framing with benchmarks on real devices to quantify proof size, generation time, verification time, and resource usage. Key findings show SNARKs deliver much smaller proofs and faster proof generation, while STARKs offer transparent, setup-free, post-quantum security with competitive verification performance depending on implementation. These insights guide developers and designers in selecting and optimizing zero-knowledge proof systems for privacy-preserving transactions, verifiable computation, and scalable rollups, highlighting where each approach excels and where further optimization is needed.

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.

Paper Structure

This paper contains 48 sections, 6 equations, 2 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Theoretical Comparison of zk-SNARKs and zk-STARKs
  3. Mathematical Foundations
  4. Representation of Computation
  5. Proof Construction
  6. Security Assumptions
  7. Efficiency and Scalability
  8. Proof Size
  9. Verification Speed
  10. Prover-Side Overheads
  11. Understanding Practical Implementation Frameworks
  12. zk-SNARK Instrumentation and Execution flow
  13. Setup (only once)
  14. Proof generation
  15. Proof verification
  16. ...and 33 more sections

Figures (2)

  • Figure 1: Comparative summary of zk-SNARK and zk-STARK performance across heterogeneous systems. Each bar represents the average proof generation and verification time measured under four architectures (ARM64, x86_64, constrained ARM, and virtualized cloud).
  • Figure 2: Cumulative CPU Time: zk-SNARK vs zk-STARK