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 …

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 …

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 …

Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile
devices. Traditional MCS platforms are based on a triangular structure consisting of three …

Data, when coupled with state-of-the-art machine learning models, can enable remarkable
applications. But, there exists an underlying tension: users wish to keep their data private …

We propose a new approach for privacy-preserving and verifiable convolutional neural
network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed …

With the rapid advancement of artificial intelligence technology, the usage of machine
learning models is gradually becoming part of our daily lives. High-quality models rely not …

The development of machine learning models requires a large amount of training data. Data
marketplace is a critical platform to trade high-quality and private-domain data that is not …

Machine learning (ML) is increasingly used behind closed systems and APIs to make
important decisions. For example, social media uses ML-based recommendation algorithms …