Paper 2021/087
ZEN: An Optimizing Compiler for Verifiable, Zero-Knowledge Neural Network Inferences
Boyuan Feng, Lianke Qin, Zhenfei Zhang, Yufei Ding, and Shumo Chu
Abstract
We present ZEN, the first optimizing compiler that generates efficient verifiable, zero-knowledge neural network inference schemes.
ZEN generates two schemes: ZEN
Note: fix abstract and author names
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint. MINOR revision.
- Keywords
- zero knowledgeneural networksprivacy
- Contact author(s)
- shumo @ cs ucsb edu
- History
- 2021-05-15: revised
- 2021-01-27: received
- See all versions
- Short URL
- https://ia.cr/2021/087
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2021/087, author = {Boyuan Feng and Lianke Qin and Zhenfei Zhang and Yufei Ding and Shumo Chu}, title = {{ZEN}: An Optimizing Compiler for Verifiable, Zero-Knowledge Neural Network Inferences}, howpublished = {Cryptology {ePrint} Archive, Paper 2021/087}, year = {2021}, url = {https://eprint.iacr.org/2021/087} }