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 …

Differential privacy provides strong privacy preservation guarantee in information sharing.
As social network analysis has been enjoying many applications, it opens a new arena for …

Differentially private analysis of graphs is widely used for releasing statistics from sensitive
graphs while still preserving user privacy. Most existing algorithms however are in a …

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 …

Triangle counting in networks under LDP (Local Differential Privacy) is a fundamental task
for analyzing connection patterns or calculating a clustering coefficient while strongly …

Differentially private algorithms allow large-scale data analytics while preserving user
privacy. Designing such algorithms for graph data is gaining importance with the growth of …

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 …