Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …

Feature selection is an efficient dimensionality reduction technique for artificial intelligence
and machine learning. Many feature selection methods learn the data structure to select the …

Nowadays, machine learning models and applications have become increasingly pervasive.
With this rapid increase in the development and employment of machine learning models, a …

The privacy-preserving data analysis has been gained significant interest across several
research communities. The current researches mainly focus on privacy-preserving …

One approach to analysis of private data is ε-differential privacy, a randomization-based
approach that protects individual data items by injecting carefully limited noise into results. A …

We study a pitfall in the typical workflow for differentially private machine learning. The use
of differentially private learning algorithms in a" drop-in" fashion--without accounting for the …

Feature selection is usually a necessary step for data mining and machine learning.
Currently, secure machine learning, especially in privacy preservation, has attracted much …

Abstract Differentially private Support Vector Machines (SVMs) have been extensively
studied in recent years. Most design mechanisms are focused on perturbing the solution to a …

Most privacy-preserving machine learning methods are designed around continuous or
numeric data, but categorical attributes are common in many application scenarios …

Privacy preserve machine learning is a hot topic in multimedia domain. In this paper, we
propose a secure multifractal feature extraction and representation method in the encrypted …