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 …

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 …

Vehicle trajectory data is critical for traffic management and location-based services.
However, the released trajectories raise serious privacy concerns because they contain …

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 …

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 …

Local differential privacy (LDP), which perturbs each user's data locally and only sends the
noisy version of her information to the aggregator, is a popular privacy-preserving data …

Machine learning service API allows model owners to monetize proprietary models by
offering prediction services to third-party users. However, existing literature shows that …

Since the emergence of coronavirus disease–2019 (COVID-19) outbreak, every country has
implemented digital solutions in the form of mobile applications, web-based frameworks …

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It
has been widely deployed in real-world systems (eg Chrome, iOS, macOS). In LDP-based …

In big data era, companies and organizations are keen to collect data from users and
analyse their behaviour patterns to make decisions or predictions for profits. However, it …