BatchZK: A Fully Pipelined GPU-Accelerated System for Batch Generation of Zero-Knowledge Proofs
Zero-knowledge proof (ZKP) is a cryptographic primitive that enables one party to prove the validity of a statement to other parties without disclosing any secret information. With its widespread adoption in applications such as blockchain and verifiable machine learning, the demand for generating zero-knowledge proofs has increased dramatically. In recent years, considerable efforts have been directed toward developing GPU-accelerated systems for proof generation. However, these previous systems only explored efficiently generating a single proof by reducing latency rather than batch generation to provide high throughput.
Authors
- Zonghui Wang (ORCID: https://orcid.org/0000-0002-8212-4618)
- Tao Lu (ORCID: https://orcid.org/0000-0002-4633-6058)
- Xiaohang Wang (ORCID: https://orcid.org/0000-0002-2263-5643)
- Yicheng Chen (ORCID: https://orcid.org/0000-0001-6369-1337)
- Wenzhi Chen (ORCID: https://orcid.org/0000-0003-1674-4701)
- J. Zhang (ORCID: https://orcid.org/0000-0002-7950-7845)
Institutions
- National University of Singapore (SG)
- Zhejiang University (CN)
Publication Details
- Published
- 2025-02-06
- DOI
- https://doi.org/10.1145/3669940.3707270
- Citations
- 3
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.83