Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

Mounting evidence has highlighted the implementation of big data handling and
management in the healthcare industry to improve the clinical services. Various private and …

In the digital healthcare era, it is of the utmost importance to harness medical information
scattered across healthcare institutions to support in-depth data analysis and achieve …

We review the use of differential privacy (DP) for privacy protection in machine learning
(ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP …

The increasing volume of personal and sensitive data being harvested by data controllers
makes it increasingly necessary to use the cloud not just to store the data, but also to …

The rapid development of wearable sensors and the 5G network empowers traditional
medical treatment with the ability to collect patients' information remotely for monitoring and …

Differential privacy is a popular privacy model within the research community because of the
strong privacy guarantee it offers, namely that the presence or absence of any individual in a …

Mining health data can lead to faster medical decisions, improvement in the quality of
treatment, disease prevention, and reduced cost, and it drives innovative solutions within the …

Releasing representative data sets without compromising the data privacy has attracted
increasing attention from the database community in recent years. Differential privacy is an …

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 …