Background Data science offers an unparalleled opportunity to identify new insights into
many aspects of human life with recent advances in health care. Using data science in …

The protection of private information is a crucial issue in data-driven research and business
contexts. Typically, techniques like anonymisation or (selective) deletion are introduced in …

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 …

Black-box machine learning models are used in an increasing number of high-stakes
domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI …

In today's world, most organizations are facing data accumulation in massive amounts and
storing them in large databases. Myriad of them, the particular healthcare industry has …

K-anonymity is the most widely used approach to privacy preserving microdata which is
mainly based on generalization. Although generalization-based k-anonymity approaches …

Data anonymization strategies such as subtree generalization have been hailed as
techniques that provide a more efficient generalization strategy compared to full-tree …

In this paper we present a formulation of k-anonymity as a mathematical optimization
problem. In solving this formulated problem, k-anonymity is achieved while maximizing the …

In the era of big data, the availability of massive amounts of information makes privacy
protection more necessary than ever. Among a variety of anonymization mechanisms …

Recent studies in data anonymization techniques have primarily focused on MapReduce.
However, these existing MapReduce based approaches often suffer from many performance …