The growing popularity and development of data mining technologies bring serious threat to
the security of individual,'s sensitive information. An emerging research topic in data mining …

The objective of machine learning is to extract useful information from data, while privacy is
preserved by concealing information. Thus it seems hard to reconcile these competing …

For meeting diverse requirements of data analysis, the machine learning classifier has been
provided as a tool to evaluate data in many applications. Due to privacy concerns of …

For many critical applications ranging from health care to social sciences, releasing
personal data while protecting individual privacy is paramount. Over the years, data …

Recent years have witnessed remarkable successes of machine learning in various
applications. However, machine learning models suffer from a potential risk of leaking …

We present a framework to statistically audit the privacy guarantee conferred by a
differentially private machine learner in practice. While previous works have taken steps …

In recent years, issues of privacy preservation in data mining and machine learning have
received more and more attention from the research community. Privacy-preserving data …

In the recent decade, deep learning techniques have been widely adopted for founding
artificial Intelligent applications, which led to successes in many data analysis tasks, such as …

Naïve Bayes classifier (NBC) is a fundamental and widely-used data mining tool. To
respond to the growing privacy concern, several privacy-preserving NBC schemes have …

Privacy preserving data classification is an important research area in data mining field. The
goal of a privacy preserving classification algorithm is to protect the sensitive information as …