The cyber physical systems integrate the sensing, computation, control and networking
processes into physical objects and infrastructure, which are connected through the Internet …

Federated Learning (FL), the de-facto distributed machine learning paradigm that locally
trains datasets at individual devices, is vulnerable to backdoor model poisoning attacks. By …

Local differential privacy (LDP) is an emerging technique for privacy-preserving data
collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be …

Vehicle trajectory data is critical for traffic management and location-based services.
However, the released trajectories raise serious privacy concerns because they contain …

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 …

Many applications rely on continual data collection to provide real-time information services,
eg, real-time road traffic forecasts. However, the collection of original data brings risks to …

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 …

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

The increasing demand for data-driven machine learning (ML) models has led to the
emergence of model markets, where a broker collects personal data from data owners to …