An Efficient and Extensible Zero-knowledge Proof Framework for Neural Networks
In recent years, cloud vendors have started to supply paid services for data analysis by
providing interfaces of their well-trained neural network models. However, customers lack …
providing interfaces of their well-trained neural network models. However, customers lack …
Zero-knowledge proofs of training for deep neural networks
A zero-knowledge proof of training (zkPoT) enables a party to prove that they have correctly
trained a committed model based on a committed dataset without revealing any additional …
trained a committed model based on a committed dataset without revealing any additional …
ZkDL: Efficient zero-knowledge proofs of deep learning training
The recent advancements in deep learning have brought about significant changes in
various aspects of people's lives. Meanwhile, these rapid developments have raised …
various aspects of people's lives. Meanwhile, these rapid developments have raised …
ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences
We present ZEN, the first optimizing compiler that generates efficient verifiable, zero-
knowledge neural network inference schemes. ZEN generates two schemes: ZEN $ _ {acc} …
knowledge neural network inference schemes. ZEN generates two schemes: ZEN $ _ {acc} …
Zkcnn: Zero knowledge proofs for convolutional neural network predictions and accuracy
Deep learning techniques with neural networks are developing prominently in recent years
and have been deployed in numerous applications. Despite their great success, in many …
and have been deployed in numerous applications. Despite their great success, in many …
[PDF][PDF] Scalable Zero-knowledge Proofs for Non-linear Functions in Machine Learning
Zero-knowledge (ZK) proofs have been recently explored for the integrity of machine
learning (ML) inference. However, these protocols suffer from high computational overhead …
learning (ML) inference. However, these protocols suffer from high computational overhead …
Validating the integrity of convolutional neural network predictions based on zero-knowledge proof
Abstract Machine Learning as a Service can provide outsourced deep learning model
prediction services to clients that do not have computing resources. Meanwhile, the integrity …
prediction services to clients that do not have computing resources. Meanwhile, the integrity …
Pipezk: Accelerating zero-knowledge proof with a pipelined architecture
Zero-knowledge proof (ZKP) is a promising cryptographic protocol for both computation
integrity and privacy. It can be used in many privacy-preserving applications including …
integrity and privacy. It can be used in many privacy-preserving applications including …
Zero-knowledge proofs for machine learning
Y Zhang - Proceedings of the 2020 Workshop on Privacy …, 2020 - dl.acm.org
Machine learning has become increasingly prominent and is widely used in various
applications in practice. Despite its great success, the integrity of machine learning …
applications in practice. Despite its great success, the integrity of machine learning …
A new zero knowledge argument for general circuits and its application
H Duan, L Xiang, X Wang, P Chu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Verifying the correctness of computation without revealing the input is a critical issue
intensively studied in real-world applications. The recent surge of zero knowledge …
intensively studied in real-world applications. The recent surge of zero knowledge …