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 …

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) …

Methods and systems for performing a computational opera tion on a server host are
provided. Exemplary methods include: receiving an encrypted service request from a client …

Secure computing methods such as fully homomorphic encryption and hardware solutions
such as Intel Software Guard Extension (SGX) have been applied to provide security for user …

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 …

Outsourcing virtual network functions (VNFs) to third-party service providers, such as public
clouds, has become the norm. While outsourcing brings many benefits, including scalability …

Abstract Fully Homomorphic Encryption (FHE) and Trusted Execution Environ-ments (TEEs)
are complementing approaches that can both secure computa-tions running remotely on a …

Increases in data production and growing demands for more computing power leads to the
current trend of outsourcing data and computation services to cloud providers. With data …

While protection of data at-rest and data in-transit can be achieved using standard
algorithms and technologies, the protection of data in-use is still, to a large extent, an open …