With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …

Neural networks (NNs) have become one of the most important tools for artificial
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …

In a model extraction attack, an adversary steals a copy of a remotely deployed machine
learning model, given oracle prediction access. We taxonomize model extraction attacks …

In domains such as health care and finance, shortage of labeled data and computational
resources is a critical issue while developing machine learning algorithms. To address the …

Applying machine learning to a problem which involves medical, financial, or other types of
sensitive data, not only requires accurate predictions but also careful attention to …

Advancements in deep learning enable cloud servers to provide inference-as-a-service for
clients. In this scenario, clients send their raw data to the server to run the deep learning …

Homomorphic Encryption (HE) is a powerful cryptographic primitive to address privacy and
security issues in outsourcing computation on sensitive data to an untrusted computation …

This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL)
framework that is built upon automated design, efficient logic synthesis, and optimization …

Machine learning models hosted in a cloud service are increasingly popular but risk privacy:
clients sending prediction requests to the service need to disclose potentially sensitive …

Neural Networks (NN) are today increasingly used in Machine Learning where they have
become deeper and deeper to accurately model or classify high-level abstractions of data …