The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

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

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

Location-based services (LBS) have been significantly developed and widely deployed in
mobile devices. It is also well-known that LBS applications may result in severe privacy …

Trajectory data has the potential to greatly benefit a wide-range of real-world applications,
such as tracking the spread of the disease through people's movement patterns and …

Vector mean estimation is a central primitive in federated analytics. In vector mean
estimation, each user i∈n holds a real-valued vector v_i∈-1,1^d, and a server wants to …

We study the problem of publishing a stream of real-valued data satisfying differential
privacy (DP). One major challenge is that the maximal possible value in the stream can be …

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 …

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 …