The growth of big data can increase risks of re-identification in complex IoT environment.
Data anonymization is widely used to prevent shared data from being re-identified private or …

Abstract The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by
enabling highly customised services and applications, which create huge volume and …

In today's digital era, it is a very common practice for organizations to collect data from
individual users. The collected data is then stored in multiple databases which contain …

Most existing works of data anonymisation target at the optimization of the anonymisation
metrics to balance the data utility and privacy, whereas they ignore the effects of a …

The topic of big data has attracted increasing interest in recent years. The emergence of big
data leads to new difficulties in terms of protection models used for data privacy, which is of …

The application of machine learning techniques to large and distributed data archives might
result in the disclosure of sensitive information about the data subjects. Data often contain …

Preserving privacy in the presence of adversary's background knowledge is very important
in data publishing. The k-anonymity model, while protecting identity, does not protect against …

Big data is collected and processed using different sources and tools that lead to privacy
issues. Privacy preserving data publishing techniques such as k‐anonymity, l‐diversity, and …

The data engineering research community has fiercely felt the requisite for privacy of data of
individuals. With technological advancement, every single of action of life is collected and …

Abstract The Internet-of-Things (IoT) produces and transmits enormous amounts of data.
Extracting valuable information from this enormous volume of data has become an important …