Training machine learning (ML) models is expensive in terms of computational power,
amounts of labeled data and human expertise. Thus, ML models constitute business value …

Time series has numerous application scenarios. However, since many time series data are
personal data, releasing them directly could cause privacy infringement. All existing …

A key factor in big data analytics and artificial intelligence is the collection of user data from a
large population. However, the collection of user data comes at the price of privacy risks, not …

Public intelligent services enabled by machine learning algorithms are vulnerable to model
extraction attacks that can steal confidential information of the learning models through …

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 …

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 …

The success of deep neural networks has contributed to many fields, such as finance, medic
and speech recognition. Machine learning models adopted in these fields are always …

Model extraction attacks are a kind of attacks where an adversary obtains a machine
learning model whose performance is comparable with one of the victim model through …

Federated learning enables multiple participants to collaboratively train a model without
aggregating the training data. Although the training data are kept within each participant and …