Most of our lives are conducted in the cyberspace. The human notion of privacy translates
into a cyber notion of privacy on many functions that take place in the cyberspace. This …

As an emerging training model with neural networks, federated learning has received
widespread attention due to its ability to update parameters without collecting users' raw …

PySyft is an open-source multi-language library enabling secure and private machine
learning by wrapping and extending popular deep learning frameworks such as PyTorch in …

Many companies provide neural network prediction services to users for a wide range of
applications. However, current prediction systems compromise one party's privacy: either the …

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness
of the large-scale impacts of AI systems, and recognition that existing regulations and norms …

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 …

To design and develop AI-based systems that users and the larger public can justifiably
trust, one needs to understand how machine learning technologies impact trust. To guide …

Advanced artificial intelligence techniques, such as federated learning, has been applied to
broad areas, eg, image classification, speech recognition, smart city, and healthcare …

Deep learning techniques with neural networks are developing prominently in recent years
and have been deployed in numerous applications. Despite their great success, in many …

With the proliferation of machine learning, the cloud server has been employed to collect
massive data and train machine learning models. Several privacy-preserving machine …