Fast-growing smart city applications, such as smart delivery, smart community, and smart
health, are generating big data that are widely distributed on the internet. IoT (Internet of …

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory
datasets. The concern is that the data may contain much inaccuracy about the actual traffic …

Anomaly detection has a wide range of real-world applications, such as bank fraud detection
and cyber intrusion detection. In the past decade, a variety of anomaly detection models …

Normalizing flow models have risen as a popular solution to the problem of density
estimation, enabling high-quality synthetic data generation as well as exact probability …

With the prevalence of mobile computing, mobile devices have been generating numerous
sensor data, aka, time series. Since these time series may include sensitive information …

This paper presents an investigation of differentially private analysis of distance-based
outliers. Outlier detection aims to identify instances that are apparently distant from other …

Location data can be extremely useful to study commuting patterns and disruptions, as well
as to predict real-time traffic volumes. At the same time, however, the fine-grained collection …

In today's world of digitization, anomaly detection has become one of the most important
issues in our lives. User and Entity Behavior Analytics (UEBA) is a security solution for …

Data analysis for emerging systems such as syndromic surveillance or intelligent
transportation systems requires testing statistical models based on privacy-sensitive data …

Time series are extensively used in finance, healthcare, IoT, and smart cities. However, in
many applications, time series often contain personal information, so releasing them publicly …