Benchmarking ZK-Friendly Hash Functions and SNARK Proving Systems for EVM-compatible Blockchains
<|reference_start|>Benchmarking ZK-Friendly Hash Functions and SNARK Proving Systems for EVM-compatible Blockchains: With the rapid development of Zero-Knowledge Proofs (ZKPs), particularly Succinct Non-Interactive Arguments of Knowledge (SNARKs), benchmarking various ZK tools has become a valuable task. ZK-friendly hash functions, as key algorithms in blockchain, have garnered significant attention. Therefore, comprehensive benchmarking and evaluations of these evolving algorithms in ZK circuits present both promising opportunities and challenges. Additionally, we focus on a popular ZKP application, privacy-preserving transaction protocols, aiming to leverage SNARKs' cost-efficiency through "batch processing" to address high on-chain costs and compliance issues.   To this end, we benchmarked three SNARK proving systems and five ZK-friendly hash functions, including our self-developed circuit templates for Poseidon2, Neptune, and GMiMC, on the bn254 curve within the circom-snarkjs framework. We also introduced the role of "sequencer" in our SNARK-based privacy-preserving transaction scheme to enhance efficiency and enable flexible auditing. We conducted privacy and security analyses, as well as implementation and evaluation on Ethereum Virtual Machine (EVM)-compatible chains. The results indicate that Poseidon and Poseidon2 demonstrate superior memory usage and runtime during proof generation under Groth16. Moreover, compared to the baseline, Poseidon2 not only generates proofs faster but also reduces on-chain costs by 73% on EVM chains and nearly 26% on Hedera. Our work provides a benchmark for ZK-friendly hash functions and ZK tools, while also exploring cost efficiency and compliance in ZKP-based privacy-preserving transaction protocols.<|reference_end|>
@article{guo2024benchmarking,
    title={Benchmarking ZK-Friendly Hash Functions and SNARK Proving Systems for
  EVM-compatible Blockchains},
    author={Hanze Guo, Yebo Feng, Cong Wu, Zengpeng Li, Jiahua Xu},
    journal={arXiv preprint arXiv:2409.01976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01976},
    primaryClass={cs.CR}
}