Local differential privacy (LDP), which perturbs each user's data locally and only sends the
noisy version of her information to the aggregator, is a popular privacy-preserving data …

With the wide applications of deep neural networks (DNNs) in various fields, current
research shows their serious security risks due to the lack of privacy protection. Observing …

Collaborative learning enables two or more participants, each with their own training
dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not …

The application of machine learning (ML) in computer systems introduces not only many
benefits but also risks to society. In this paper, we develop the concept of ML governance to …

Machine-Learning-as-a-Service providers expose machine learning (ML) models through
application programming interfaces (APIs) to developers. Recent work has shown that …

The energy industry is undergoing significant transformations as it strives to achieve net-
zero emissions and future-proof its infrastructure, where every participant in the power grid …

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It
has been widely deployed in real-world systems (eg Chrome, iOS, macOS). In LDP-based …

Machine Learning as a Service (MLaaS) platforms have gained popularity due to their
accessibility, cost-efficiency, scalability, and rapid development capabilities. However …

Abstract Local Differential Privacy (LDP), where each user perturbs her data locally before
sending to an untrusted party, is a new and promising privacy-preserving model. Endorsed …

Despite their stunning performance, developing deep learning models from scratch is a
formidable task. Therefore, it popularizes Machine-Learning-as-a-Service (MLaaS), where …