Zk-snarks Generate Proofs 68x Faster and Reduce Size 123x Compared to zk-STARKs, Study Shows
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Zero-knowledge proofs underpin increasingly important technologies that enable privacy and secure computation, and two leading frameworks, zk-SNARKs and zk-STARKs, offer different approaches to achieving this. Ayush Nainwal, Atharva Kamble, and Nitin Awathare from the Indian Institute of Technology, Jodhpur, lead a comprehensive investigation into the practical performance of these systems, moving beyond theoretical comparisons to examine real-world implementation. Their work systematically evaluates proof generation speed, verification time, and proof size using publicly available code on standard hardware, revealing significant trade-offs between the two approaches.
The team demonstrates that zk-SNARKs currently generate proofs much faster and with smaller file sizes, but require a trusted initial setup, while zk-STARKs offer transparency and faster verification despite slower proof creation, providing crucial data for developers building privacy-focused applications and scalable blockchain technologies. Efficient Zero-Knowledge Proofs With STARKs and SNARKs This study details the implementation of high-performance zero-knowledge proof systems, focusing on both STARK (Scalable Transparent ARgument of Knowledge) and SNARK (Succinct Non-interactive ARgument of Knowledge) constructions. The goal is to efficiently verify computations without revealing the underlying data, a crucial capability for privacy-preserving applications, scalable blockchains, and secure computation. Researchers cover the mathematical foundations, algorithmic details, and optimization techniques used in a specific implementation, aiming to provide a comprehensive understanding of these powerful tools. STARKs and SNARKs represent distinct approaches to zero-knowledge proofs. STARKs are transparent, eliminating the need for a trusted setup phase and enhancing security, though generally produce larger proofs. SNARKs, conversely, are succinct, generating small proof sizes, but typically require a trusted setup, which, if compromised, could allow the creation of false proofs. Groth16 is a popular SNARK construction used in this implementation. Both systems rely on representing computations as algebraic constraints. R1CS (Rank-1 Constraint System) and QAP (Quadratic Arithmetic Program) allow for efficient manipulation and evaluation. A key component of STARKs is the use of polynomial commitments, enabling verification of polynomials without revealing their coefficients, and the FRI (Fast Reed-Solomon Interactive Oracle Proof of Proximity) protocol provides efficient and post-quantum secure low-degree testing. Groth16, used in SNARKs, employs elliptic curve pairings for efficient proof verification, requiring carefully generated proving and verification keys. Researchers demonstrated the system using a Fibonacci-like recurrence relation as an example computation, representing the execution trace as a polynomial and encoding the recurrence relation as a constraint. This allows for a clear demonstration of how the proof system operates. The study emphasizes the importance of scalability, security, and efficiency in zero-knowledge proof systems, and provides a valuable resource for anyone interested in learning more about these technologies and their applications. Practical Comparison of zk-SNARKs and zk-STARKs This research pioneers a systematic, implementation-level comparison of zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARKs) and zero-knowledge Scalable Transparent ARguments of Knowledge (zk-STARKs) using publicly available reference implementations on a consumer-grade Apple M1 ARM laptop. Researchers meticulously evaluated proof generation time, verification latency, and proof size to quantify the practical trade-offs between these two prominent proof systems.
The team discovered zk-SNARKs generated proofs 68times faster, completing the process in 55 milliseconds compared to 3. 809 seconds for zk-STARKs. Conversely, zk-STARKs verified proofs significantly faster, requiring 0. 472 milliseconds versus 1807 milliseconds for zk-SNARKs. Proof size analysis demonstrated zk-SNARKs produced substantially smaller proofs, measuring 0. 6 kilobytes compared to 69 kilobytes for zk-STARKs. Further CPU profiling revealed distinct computational bottlenecks for each system. Researchers found zk-SNARKs performance was limited by pairing and field arithmetic operations, while zk-STARKs were constrained by polynomial and big-integer operations. This highlights how implementation choices and underlying mathematical structures heavily influence real-world performance characteristics. The study demonstrates that while zk-SNARKs excel in proof generation speed and compactness, zk-STARKs offer faster verification and eliminate the need for a trusted setup, providing valuable insights for developers and protocol designers selecting proof systems for privacy-preserving applications. Zk-SNARKs and zk-STARKs Performance on ARM Hardware This work presents a systematic comparison of two leading zero-knowledge proof systems, zk-SNARKs and zk-STARKs, using publicly available implementations on a consumer-grade ARM platform. Researchers conducted a detailed empirical evaluation focusing on proof generation time, verification latency, and proof size to understand performance under real-world conditions. Key findings demonstrate that zk-SNARKs generate proofs 68times faster, achieving generation times of 55 milliseconds compared to 3. 809 seconds for zk-STARKs, and produce proofs 123times smaller, measuring 0. 6 kilobytes versus 69 kilobytes. However, zk-SNARKs require a trusted setup and exhibit slower verification times of 1807 milliseconds, while zk-STARKs verify faster at 0. 472 milliseconds and remain transparent, eliminating the need for a trusted setup. The study’s CPU profiling revealed distinct computational bottlenecks for each system, underscoring the impact of implementation details on performance. zk-SNARKs experienced limitations in pairing and field arithmetic, while zk-STARKs were constrained by polynomial and big-integer operations. These findings highlight how execution models and specific implementation choices significantly affect real-world performance, providing actionable insights for developers and protocol designers. Researchers evaluated both protocols on an Apple M1 laptop, utilizing publicly available reference implementations to ensure transparent baselines for comparison. This detailed analysis provides concrete data to inform the selection and optimization of proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups. Zk-SNARKs and zk-STARKs, a Hardware Comparison This research presents a detailed, implementation-level comparison of two leading zero-knowledge proof systems, zk-SNARKs and zk-STARKs, across diverse hardware platforms. The study demonstrates that zk-SNARKs currently achieve significantly faster proof generation times and produce substantially smaller proofs compared to zk-STARKs. However, zk-STARKs offer the advantage of transparent setup and avoid the need for a trusted third party, a crucial benefit for certain applications. The findings reveal distinct performance characteristics for each system, with zk-SNARK generation times varying considerably across different devices, while zk-STARK verification times remained relatively consistent. Analysis of the implementations highlights that the larger proof size of zk-STARKs stems from its reliance on hash-based commitments and the FRI protocol, and the slower generation times are linked to extensive polynomial evaluations. The authors acknowledge that the observed performance differences are influenced by specific implementation choices. 👉 More information 🗞 A Comparative Analysis of zk-SNARKs and zk-STARKs: Theory and Practice 🧠 ArXiv: https://arxiv.org/abs/2512.10020 Tags: