zkVC: Fast Zero-Knowledge Proof for Private and Verifiable Computing

In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the computation, data and results are prone to tampering due to the vulnerabilities on the server side. Thus, verifying the integ...

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Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Zhang, Yancheng, Zheng, Mengxin, Chen, Xun, Hu, Jingtong, Shi, Weidong, Ju, Lei, Solihin, Yan, Lou, Qian
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Cloud computing
Computational efficiency
Computational modeling
Design automation
Intellectual property
Machine learning
Polynomials
Privacy
Private and Verifiable Computing
Servers
Transformers
Zero-Knowledge Proof
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Abstract In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the computation, data and results are prone to tampering due to the vulnerabilities on the server side. Thus, verifying the integrity of computation is important in the client-server setting. The cryptographic method known as Zero-Knowledge Proof (ZKP) is renowned for facilitating private and verifiable computing. ZKP allows the client to validate that the results from the server are computed correctly without violating the privacy of the server's intellectual property. Zero-Knowledge Succinct NonInteractive Argument of Knowledge (zkSNARKs), in particular, has been widely applied in various applications like blockchain and verifiable machine learning. Despite their popularity, existing zkSNARKs approaches remain highly computationally intensive. For instance, even basic operations like matrix multiplication require an extensive number of constraints, resulting in significant overhead. In addressing this challenge, we introduce z k V C, which optimizes the ZKP computation for matrix multiplication, enabling rapid proof generation on the server side and efficient verification on the client side. zkVC integrates optimized ZKP modules, such as Constraint-reduced Polynomial Circuit (CRPC) and Prefix-Sum Query (PSQ), collectively yielding a more than \mathbf{1 2}-fold increase in proof speed over prior methods. The code is available at https://github.com/UCF-Lou-Lab-PET/zkformer.
AbstractList In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the computation, data and results are prone to tampering due to the vulnerabilities on the server side. Thus, verifying the integrity of computation is important in the client-server setting. The cryptographic method known as Zero-Knowledge Proof (ZKP) is renowned for facilitating private and verifiable computing. ZKP allows the client to validate that the results from the server are computed correctly without violating the privacy of the server's intellectual property. Zero-Knowledge Succinct NonInteractive Argument of Knowledge (zkSNARKs), in particular, has been widely applied in various applications like blockchain and verifiable machine learning. Despite their popularity, existing zkSNARKs approaches remain highly computationally intensive. For instance, even basic operations like matrix multiplication require an extensive number of constraints, resulting in significant overhead. In addressing this challenge, we introduce z k V C, which optimizes the ZKP computation for matrix multiplication, enabling rapid proof generation on the server side and efficient verification on the client side. zkVC integrates optimized ZKP modules, such as Constraint-reduced Polynomial Circuit (CRPC) and Prefix-Sum Query (PSQ), collectively yielding a more than \mathbf{1 2}-fold increase in proof speed over prior methods. The code is available at https://github.com/UCF-Lou-Lab-PET/zkformer.
Author Shi, Weidong
Lou, Qian
Zheng, Mengxin
Zhang, Yancheng
Chen, Xun
Solihin, Yan
Hu, Jingtong
Ju, Lei
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  givenname: Yancheng
  surname: Zhang
  fullname: Zhang, Yancheng
  organization: University of Central Florida
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  givenname: Mengxin
  surname: Zheng
  fullname: Zheng, Mengxin
  organization: University of Central Florida
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  fullname: Shi, Weidong
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  surname: Ju
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  givenname: Yan
  surname: Solihin
  fullname: Solihin, Yan
  organization: University of Central Florida
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  givenname: Qian
  surname: Lou
  fullname: Lou, Qian
  email: qian.lou@ucf.edu
  organization: University of Central Florida
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SubjectTerms Cloud computing
Computational efficiency
Computational modeling
Design automation
Intellectual property
Machine learning
Polynomials
Privacy
Private and Verifiable Computing
Servers
Transformers
Zero-Knowledge Proof
Title zkVC: Fast Zero-Knowledge Proof for Private and Verifiable Computing
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