Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

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 …

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 …

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 …

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 …

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against
privacy attacks. Different privacy levels regarding users' attitudes need to be satisfied locally …

Federated learning (FL) enables distributed participants to collaboratively learn a global
model without revealing their private data to each other. Recently, vertical FL, where the …