Zero-knowledge proof is a powerful cryptographic primitive that has found various
applications in the real world. However, existing schemes with succinct proof size suffer from …

With the proliferation of machine learning, the cloud server has been employed to collect
massive data and train machine learning models. Several privacy-preserving machine …

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 …

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 …

How can a model owner prove they trained their model according to the correct
specification? More importantly, how can they do so while preserving the privacy of the …

Federated machine learning (FL) allows to collectively train models on sensitive data as only
the clients' models and not their training data need to be shared. However, despite the …

Elections are an important corner stone of democratic processes. In addition to publishing
the final result (eg, the overall winner), elections typically publish the full tally consisting of all …

As ML models have increased in capabilities and accuracy, so has the complexity of their
deployments. Increasingly, ML model consumers are turning to service providers to serve …

We propose a new doubly efficient interactive proof protocol for general arithmetic circuits.
The protocol generalizes the interactive proof for layered circuits proposed by Goldwasser …

Machine unlearning aims to remove points from the training dataset of a machine learning
model after training; for example when a user requests their data to be deleted. While many …