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 …

The fast development of the Internet and mobile devices results in a crowdsensing business
model, where individuals (users) are willing to contribute their data to help the institution …

Local Differential Privacy (LDP) protocols enable an untrusted server to perform privacy-
preserving, federated data analytics. Various LDP protocols have been developed for …

Local Differential Privacy (LDP) is a popular standard for privacy-preserving data collection.
Numerous LDP protocols have been proposed in the literature which differ in how they …

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 …

In this paper, we tackle the problem of answering multi-dimensional range queries under
local differential privacy. There are three key technical challenges: capturing the correlations …

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 …

As machine learning and artificial intelligence (ML/AI) are becoming more popular and
advanced, there is a wish to turn sensitive data into valuable information via ML/AI …