There is an urgent demand for privacy-preserving techniques capable of supporting
compute and data intensive (CDI) computing in the era of big data. However, none of …

With its huge real-world demands, large-scale confidential computing still cannot be
supported by today's Trusted Execution Environment (TEE), due to the lack of scalable and …

With the trend of processing a large volume of sensitive data on PaaS services (eg, DNN
training), a TEE architecture that supports general heterogeneous accelerators, enables …

The recent edge computing infrastructure introduces a new computing model that works as a
complement of the traditional cloud computing. The edge nodes in the infrastructure reduce …

The use of trusted hardware has become a promising solution to enable privacy-preserving
machine learning. In particular, users can upload their private data and models to a …

Trusted execution environments (TEEs) are primary enablers of confidential computing. This
paper presents a systematization of the existing trusted execution environments in industry …

This paper proposes a new confidential deep learning (DL) inference system with ARM
TrustZone to provide confidentiality and integrity of DL models and data in an untrusted …

Modern data centers have grown beyond CPU nodes to provide domain-specific
accelerators such as GPUs and FPGAs to their customers. From a security standpoint, cloud …

This study identifies and proposes techniques to alleviate two key bottlenecks to executing
deep neural networks in trusted execution environments (TEEs): page thrashing during the …

Machine learning applications are getting prevelent on various computing platforms,
including cloud servers, smart phones, IoT devices, etc. For these applications, security is …