In this article, we survey existing academic and commercial efforts to provide Field-
Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a …

Privacy and security have rapidly emerged as first order design constraints. Users now
demand more protection over who can see their data (confidentiality) as well as how it is …

This paper presents MAXelerator, the first hardware accelerator for privacy-preserving
machine learning (ML) on cloud servers. Cloud-based ML is being increasingly employed in …

We present FASE, an FPGA accelerator for Secure Function Evaluation (SFE) by employing
the well-known cryptographic protocol named Yao's Garbled Circuit (GC). SFE allows two …

Data privacy is an increasing concern in our interconnected world. Garbled circuits is an
important approach used for Secure Function Evaluation (SFE); however it suffers from long …

Privacy has rapidly become a major concern/design consideration. Homomorphic
Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support …

We present a relational MPC framework for secure collaborative analytics on private data
with no information leakage. Our work targets challenging use cases where data owners …

Multi-Party Computation (MPC) is a technique enabling data from several sources to be
used in a secure computation revealing only the result while protecting the original data …

Performance of distributed data center applications can be improved through use of FPGA-
based SmartNICs, which provide additional functionality and enable higher bandwidth …

Multi-Party Computation (MPC) is an important technique used to enable computation over
confidential data from several sources. The public cloud provides a unique opportunity to …