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 …

The collection and analysis of data are continuously growing due to the pervasiveness of
computing devices. The analysis of such information is fostering businesses and …

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 …

Data privacy is a stringent need when sharing and processing data on a distributed
environment or in Internet of Things. Collaborative privacy-preserving data mining based on …

Big data refers to a massive volume of data collected from heterogeneous data sources
including data collected from Internet of Things (IoT) devices. Big data analytics is playing a …

Data processing techniques and the growth of the internet have resulted in a data explosion.
The data that are now available may contain sensitive information that could, if misused …

Due to raising concerns about the privacy preserving of personal information, organizations
which are using the customers' records in data mining activities are rammed to take actions …

In the recently age, the volume of information is growing exponentially. This data can be
used in several fields such as business, healthcare, cyber security, etc. Extracting useful …

As a serious concern in data publishing and analysis, privacy preservation of individuals has
received much attentions. Anonymity models via generalization can protect individual …

Releasing, publishing or transferring microdata is restricted by the necessity to protect the
privacy of data owners. k-anonymity is one of the most widespread concepts for …