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 …

As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Deep learning can achieve higher accuracy than traditional machine learning algorithms in
a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn …

Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …

Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …

Smart agriculture systems based on Internet of Things are the most promising to increase
food production and reduce the consumption of resources like fresh water. In this study, we …

As a new term in the financial industry, FinTech has become a popular term that describes
novel technologies adopted by the financial service institutions. This term covers a large …

Machine learning algorithms based on deep neural networks have achieved remarkable
results and are being extensively used in different domains. However, the machine learning …

The rise of machine learning as a service multiplies scenarios where one faces a privacy
dilemma: either sensitive user data must be revealed to the entity that evaluates the …