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 …

Deep neural networks have been widely applied in various machine learning applications
for mobile data analytics in cloud. However, this approach introduces significant data …

Deep learning holds a great promise of revolutionizing healthcare and medicine.
Unfortunately, various inference attack models demonstrated that deep learning puts …

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 …

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 …

This paper considers the scenario that multiple data owners wish to apply a machine
learning method over the combined dataset of all owners to obtain the best possible …

To improve the efficiency of big data feature learning, the paper proposes a privacy
preserving deep computation model by offloading the expensive operations to the cloud …

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 …

Deep learning is one of the advanced approaches of machine learning, and has attracted a
growing attention in the recent years. It is used nowadays in different domains and …

Federated learning is a recently proposed paradigm that presents significant advantages in
privacy-preserving machine learning services. It enables the deep learning applications on …