The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …

Synthetic datasets are gradually emerging as solutions for data sharing. Multiple synthetic
data generators have been introduced in the last decade fueled by advancement in machine …

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of
healthcare data for secondary purposes. It is considered a safe approach for the sharing of …

Background Synthetic tabular data generation is a potentially valuable technology with great
promise for data augmentation and privacy preservation. However, prior to adoption, an …

Artificial intelligence (AI) is a transformative technology with a substantial number of practical
applications in commercial sectors such as healthcare, finance, aviation, and smart cities. AI …

To date, the use of synthetic data generation techniques in the health and wellbeing domain
has been mainly limited to research activities. Although several open source and …

Synthetic data (SD) have garnered attention as a privacy enhancing technology.
Unfortunately, there is no standard for assessing their degree of privacy protection. In this …

The collection of user attributes by service providers is a double-edged sword. They are
instrumental in driving statistical analysis to train more accurate predictive models like …

AI-based data synthesis has seen rapid progress over the last several years and is
increasingly recognized for its promise to enable privacy-respecting high-fidelity data …

A major hurdle in the development of natural language processing (NLP) methods for
Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns …