Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

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 …

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

In recent years, deep learning achieves remarkable results in the field of artificial
intelligence. However, the training process of deep neural networks may cause the leakage …

Nowadays, deep learning has many applications in our daily life such as self-driving,
product recommendation, advertisements and healthcare. In the training phase, deep …

Deployment of deep learning in different fields and industries is growing day by day due to
its performance, which relies on the availability of data and compute. Data is often crowd …

The unprecedented success of deep learning is largely dependent on the availability of
massive amount of training data. In many cases, these data are crowd-sourced and may …

In this paper, we apply machine learning to distributed private data owned by multiple data
owners, entities with access to non-overlapping training datasets. We use noisy …

We detail a new framework for privacy preserving deep learning and discuss its assets. The
framework puts a premium on ownership and secure processing of data and introduces a …

With the popularity of smart devices and the widespread use of machine learning methods,
smart edges have become the mainstream of dealing with wireless big data. When smart …