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 …

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 …

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 …

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …

Abstract Local Differential Privacy (LDP) can provide each user with strong privacy
guarantees under untrusted data curators while ensuring accurate statistics derived from …

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic
discovery of text data and serves as a fundamental tool for text analysis in various …

A statistical hypothesis test determines whether a hypothesis should be rejected based on
samples from populations. In particular, randomized controlled experiments (or A/B testing) …

The objective of differential privacy (DP) is to protect privacy by producing an output
distribution that is indistinguishable between any two neighboring databases. However …

Traditional differential privacy is independent of the data distribution. However, this is not
well-matched with the modern machine learning context, where models are trained on …

Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …