Location-based services (LBSs) provide enhanced functionality and convenience of
ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …

Along with the rapid development of network technologies, the server-side of an e-
commerce website is becoming more and more untrustworthy. Thus, how to prevent the …

Destination prediction plays an important role as the basis for a variety of location-based
services (LBSs). However, it poses many threats to users' location privacy. Most related work …

With the wide use of GPS enabled devices and Location-Based Services, location privacy
has become an increasingly worrying challenge to our community. Existing approaches …

The widespread adoption of continuously connected smartphones and tablets developed
the usage of mobile applications, among which many use location to provide geolocated …

Directional distribution analysis has long served as a fundamental functionality in
abstracting dispersion and orientation of spatial datasets. Spatial datasets that describe …

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 …

We survey the literature on the privacy of trajectory micro-data, ie, spatiotemporal
information about the mobility of individuals, whose collection is becoming increasingly …