Although users can obtain various services by sharing their location information online with
location-based service providers, it reveals sensitive information about users. However …

Trajectory analysis holds many promises, from improvements in traffic management to
routing advice or infrastructure development. However, learning users' paths is extremely …

The computational cost for implementing data privacy protection tends to rise as the
dimensions increase, especially on correlated datasets. For this reason, a faster data …

The privacy risks of processing human locations and their trajectories have been
demonstrated by a large number of studies and real-world incidents. As a result, many …

Federated learning (FL) aims to address the challenges of data silos and privacy protection
in artificial intelligence. Vertically federated learning (VFL) with independent feature spaces …

Gait recognition is the process of identifying humans from their bipedal locomotion such as
walking or running. As such, gait data is privacy sensitive information and should be …

Differential privacy (DP) has achieved great progress in addressing the user privacy
preservation issues related to data analysis in the Internet of Things (IoT) services and …

Differential privacy ensures the security of individual privacy but poses challenges to data
exploration processes because the limited privacy budget incapacitates the flexibility of …

Quantum computing offers unparalleled processing power but raises significant data privacy
challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to …

Differential privacy (DP) is widely employed for the private data release in the single-party
scenario. Data utility could be degraded with noise generated by ubiquitous data correlation …