相关论文: TGh: A TEE/GC Hybrid Enabling Confidential FaaS Pl…

The blockchain-based smart contract lacks privacy since the contract state and instruction code are exposed to the public. Combining smart-contract execution with Trusted Execution Environments (TEEs) provides an efficient solution, called…

Security architectures providing Trusted Execution Environments (TEEs) have been an appealing research subject for a wide range of computer systems, from low-end embedded devices to powerful cloud servers. The goal of these architectures is…

Trusted executions environments (TEEs) such as Intel(R) SGX provide hardware-isolated execution areas in memory, called enclaves. By running only the most trusted application components in the enclave, TEEs enable developers to minimize the…

Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic attention in recent years. Implementing the TEE on the server…

When neural network model and data are outsourced to cloud server for inference, it is desired to preserve the confidentiality of model and data as the involved parties (i.e., cloud server, model providing client and data providing client)…

Modern computer systems tend to rely on large trusted computing bases (TCBs) for operations. To address the TCB bloating problem, hardware vendors have developed mechanisms to enable or facilitate the creation of a trusted execution…

Confidential computing (CC) or trusted execution enclaves (TEEs) is now the most common approach to enable secure computing in the cloud. The recent introduction of GPU TEEs by NVIDIA enables machine learning (ML) models to be trained…

Fully homomorphic encryption (FHE) and trusted execution environments (TEE) are two approaches to provide confidentiality during data processing. Each approach has its own strengths and weaknesses. In certain scenarios, computations can be…

Nowadays, enterprises widely deploy Network Functions (NFs) and server applications in the cloud. However, processing of sensitive data and trusted execution cannot be securely deployed in the untrusted cloud. Cloud providers themselves…

Trusted Execution Environment, or enclave, promises to protect data confidentiality and execution integrity of an outsourced computation on an untrusted host. Extending the protection to distributed applications that run on physically…

Heterogeneous collaborative computing with NPU and CPU has received widespread attention due to its substantial performance benefits. To ensure data confidentiality and integrity during computing, Trusted Execution Environments (TEE) is…

Data hosted in a cloud environment can be subject to attacks from a higher privileged adversary, such as a malicious or compromised cloud provider. To provide confidentiality and integrity even in the presence of such an adversary, a number…

Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others, two unique challenges: how to protect privacy of the…

FPGAs are now used in public clouds to accelerate a wide range of applications, including many that operate on sensitive data such as financial and medical records. We present ShEF, a trusted execution environment (TEE) for cloud-based…

Hardware-based Trusted Execution Environments (TEEs) are becoming increasingly prevalent in cloud computing, forming the basis for confidential computing. However, the security goals of TEEs sometimes conflict with existing cloud…

The classification service over a stream of data is becoming an important offering for cloud providers, but users may encounter obstacles in providing sensitive data due to privacy concerns. While Trusted Execution Environments (TEEs) are…

Trusted Execution Environments (TEEs) protect sensitive code and data from the operating system, hypervisor, or other untrusted software. Different solutions exist, each proposing different features. Abstraction layers aim to unify the…

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple clients to jointly train a model to take benefits from diverse datasets from the clients without sharing their local training datasets. FL helps reduce…

The rapid development of Internet of Things (IoT) technology has led to growing concerns about data security and user privacy in the interactions within distributed systems. Decentralized Applications (DApps) in distributed systems consist…

Trusted execution environments (TEE) such as Intel's Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensitive data such as human genomics. However, a performance…