Siniel: Distributed Privacy-Preserving zkSNARK

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information.However, existing zkSNARKs suffer from high computation overhead in the proof generation.This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials.Private delegation has become a prominent way to accelerate proof generation.In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS).Our protocol allows a computationally limited prover (a.k.a.delegator) to delegate its expensive prover computation to several workers without leaking any information about the private witness.Most importantly, compared with the recent work EOS (USENIX'23), the state-of-the-art zkSNARK prover delegation framework, a prover in Siniel needs not to engage in the MPC protocol after sending its shares of private witness.This means that a Siniel prover can outsource the entire computation to the workers.We compare Siniel with EOS and show significant performance advantages of the former.The experimental results show that, under low bandwidth conditions (10MBps), Siniel saves about 16% time for delegators than that of EOS, whereas under high bandwidth conditions (1000MBps), Siniel saves about 80% than EOS.

Authors

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.14722/ndss.2025.240152
Citations
3
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
7.44

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Siniel: Distributed Privacy-Preserving zkSNARK

Robert H. Deng, Zhenfu Cao, Jianfei Sun, Jiachen Shen et al.
3 citations
Privacy-Preserving Technologies in Data
7.44
article

Siniel: Distributed Privacy-Preserving zkSNARK

Robert H. Deng, Zhenfu Cao, Jianfei Sun, Jiachen Shen, Kailun Wang, Xiaolei Dong, Guomin Yang, Xiaoguo Li, Yu Cheng, Yunbo Yang
article en
3 citations

Abstract

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information.However, existing zkSNARKs suffer from high computation overhead in the proof generation.This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials.Private delegation has become a prominent way to accelerate proof generation.In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS).Our protocol allows a computationally limited prover (a.k.a.delegator) to delegate its expensive prover computation to several workers without leaking any information about the private witness.Most importantly, compared with the recent work EOS (USENIX'23), the state-of-the-art zkSNARK prover delegation framework, a prover in Siniel needs not to engage in the MPC protocol after sending its shares of private witness.This means that a Siniel prover can outsource the entire computation to the workers.We compare Siniel with EOS and show significant performance advantages of the former.The experimental results show that, under low bandwidth conditions (10MBps), Siniel saves about 16% time for delegators than that of EOS, whereas under high bandwidth conditions (1000MBps), Siniel saves about 80% than EOS.

National Natural Science Foundation of China, AXA Research Fund, Singapore Management University, National Key Research and Development Program of China
Openalex Percentile: Top 4%
Privacy-Preserving Technologies in Data
7.44
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.