The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

Federated Learning (FL) relies on on-device training to avoid the migration of devices' data
to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios …

In this paper, the application of federated learning to smart farming has been investigated.
The Federated averaging model has been used to carry out crop classification using climatic …

This work presents a novel protocol for fast secure inference of neural networks applied to
computer vision applications. It focuses on improving the overall performance of the online …

Particularly when creating cloud apps and web services, the cybersecurity of cloud services
plays a crucial role. Use of the internet is required for cloud computing; Consequently, there …

With the increasing use of AI in high-stakes domains such as medicine, law, and defense,
organizations spend a lot of time and money to make ML models trustworthy. Many books on …

We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for
differentially private (DP) machine learning (ML). Optimal noise calibration in this setting …

The ever growing use of machine learning (ML), motivated by the possibilities it brings to a
large number of sectors, is increasingly raising questions because of the sensitive nature of …

PyDentity lowers the entry barrier for parties interested in experimenting with the
Hyperledger's verifiable information exchange platform. It enables educators, developers …

The field of privacy preserving machine learning is still in its infancy and has been growing
in popularity since 2019. Privacy enhancing technologies within the context of machine …