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 …

Nowadays, deep learning has been increasingly applied in real-world scenarios involving
the collection and analysis of sensitive data, which often causes privacy leakage. Differential …

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data
publishing that addresses the shortcomings of traditional anonymisation techniques. The …

The large-scale monitoring of computer users' software activities has become commonplace,
eg, for application telemetry, error reporting, or demographic profiling. This paper describes …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

Differential privacy (DP) provides formal guarantees that the output of a database query
does not reveal too much information about any individual present in the database. While …

In differential privacy (DP), a challenging problem is to generate synthetic datasets that
efficiently capture the useful information in the private data. The synthetic dataset enables …

For many critical applications ranging from health care to social sciences, releasing
personal data while protecting individual privacy is paramount. Over the years, data …

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