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 …

Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

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 …

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of
development of information and communication technologies (ICT). With the provision of …

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 …

Critical infrastructures such as healthcare, intelligent transportation, and home automation
adapt billions of smart IoT devices to handle real-time data effectively in the digital era. The …

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 …

Camouflaging user's actual location with fakes is a prevalent obfuscation technique for
protecting location privacy. We show that the protection mechanisms based on the existing …

GPS-enabled devices are now ubiquitous, from airplanes and cars to smartphones and
wearable technology. This has resulted in a wealth of data about the movements of …

As the prevalence of data analysis grows, safeguarding data privacy has become a
paramount concern. Consequently, there has been an upsurge in the development of …