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 …

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 …

The widespread adoption of deep learning is facilitated in part by the availability of large-
scale data for training desirable models. However, these data may involve sensitive …

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 …

In global data analysis, the central server needs the global statistic of the user data stored in
local clients. In such cases, an Honest-but-Curious central server might put user privacy at …

Online service providers benefit from collecting and analyzing preference data from users,
including both implicit preference data (eg, watched videos of a user) and explicit preference …

Local Differential Privacy (LDP) allows answering queries on users data while maintaining
their privacy. Queries are often issued on multidimensional datasets with categorical and …

Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and
analyze sensitive data while preserving users' privacy. However, almost all LDP protocols …

The objective of differential privacy (DP) is to protect privacy by producing an output
distribution that is indistinguishable between any two neighboring databases. However …