Data confidentiality is a central concern in modern computer systems and services, as
sensitive data from users and companies are being increasingly delegated to such systems …

In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph
deep learning (DL) compiler, that enables data scientists to deploy models with popular …

Data, when coupled with state-of-the-art machine learning models, can enable remarkable
applications. But, there exists an underlying tension: users wish to keep their data private …

Protecting data-in-use from privileged attackers is challenging. New CPU extensions
(notably: Intel SGX) and cryptographic techniques (specifically: Homomorphic Encryption) …

Homomorphic encryption (HE) protects data in-use, but can be computationally expensive.
To avoid the costly bootstrapping procedure that refreshes ciphertexts, some works have …

The shift towards efficient and automated data analysis through Machine Learning (ML) has
notably impacted healthcare systems, particularly Radiomics. Radiomics leverages ML to …

With the rapid advancements in machine learning and the widespread adoption of Model-as-
a-Service (MaaS) platforms, there has been significant attention on convolutional neural …

Cloud computing has been a prominent technology that allows users to store their data and
outsource intensive computations. However, users of cloud services are also concerned …

Federated learning (FL), a decentralized machine learning technique, enhances privacy by
enabling multiple devices to collaboratively train a model without transferring data to a …

With the increasing breaches and security threats that endanger health data, ensuring
patients' privacy is essential. To that end, the research community has proposed various …