Differential privacy (DP) has been widely explored in academia recently but less so in
industry possibly due to its strong privacy guarantee. This paper makes the first attempt to …

Federated recommendation can potentially alleviate the privacy concerns in collecting
sensitive and personal data for training personalized recommendation systems. However, it …

Abstract The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model
for various tasks in recent years. In this paper, we study how to improve model accuracy of …

The Internet of Things (IoT) is transforming major industries, including but not limited to
healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually …

Data mining has heralded the major breakthrough in data analysis, serving as a “super
cruncher” to discover hidden information and valuable knowledge in big data systems. For …

Privacy preserving machine learning algorithms are crucial for learning models over user
data to protect sensitive information. Motivated by this, differentially private stochastic …

In the big data era, the generation of data presents some new characteristics, including wide
distribution, high velocity, high dimensionality, and privacy concern. To address these …

Deep learning models are increasingly popular in many machine learning applications
where the training data may contain sensitive information. To provide formal and rigorous …

The growing number of mobile and IoT devices has nourished many intelligent applications.
In order to produce high-quality machine learning models, they constantly access and …

Nowadays, deep learning has been increasingly applied in real-world scenarios involving
the collection and analysis of sensitive data, which often causes privacy leakage. Differential …