Related papers: A Survey of Secure Computation Using Trusted Execu…
Process attestation systems verify that a continuous physical process, such as human authorship, actually occurred, rather than merely checking system state. These systems face a fundamental dependability challenge: the evidence collection…
Trusted Execution Environments (TEEs) are hardware-enforced memory isolation units, emerging as a pivotal security solution for security-critical applications. TEEs, like Intel SGX and ARM TrustZone, allow the isolation of confidential code…
Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current…
Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have…
Federated Learning (FL) is a distributed machine learning approach that has emerged as an effective way to address recent privacy concerns. However, FL introduces the need for additional security measures as FL alone is still subject to…
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…
Trusted Execution Environments (TEEs) have become a cornerstone of confidential computing, attracting significant attention from academia and industry. To support secure and scalable application deployment on confidential clouds, TEE…
Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees.…
The growing replication crisis across disciplines such as economics, finance, and other social sciences as well as computer science undermines the credibility of academic research. Current institutional solutions -- such as artifact…
Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential…
The increasing adoption of Large Language Models (LLMs) in cloud environments raises critical security concerns, particularly regarding model confidentiality and data privacy. Confidential computing, enabled by Trusted Execution…
With the increasing popularity of Internet of Things (IoT) devices, security concerns have become a major challenge: confidential information is constantly being transmitted (sometimes inadvertently) from user devices to untrusted cloud…
A niche corner of the Web3 world is increasingly making use of hardware-based Trusted Execution Environments (TEEs) to build decentralized infrastructure. One of the motivations to use TEEs is to go beyond the current performance…
Trusted Execution Environment (TEE) enhances the security of mobile applications and cloud services by isolating sensitive code in the secure world from the non-secure normal world. However, TEE applications are still confronted with…
Trusted execution environment (TEE) technology has found many applications in mitigating various security risks in an efficient manner, which is attractive for critical infrastructure protection. First, the natural of critical…
To ensure secure and trustworthy execution of applications, vendors frequently embed trusted execution environments into their systems. Here, applications are protected from adversaries, including a malicious operating system. TEEs are…
New types of Trusted Execution Environment (TEE) architectures like TrustLite and Intel Software Guard Extensions (SGX) are emerging. They bring new features that can lead to innovative security and privacy solutions. But each new TEE…
In cloud computing, data processing is delegated to a remote party for efficiency and flexibility reasons. A practical user requirement usually is that the confidentiality and integrity of data processing needs to be protected. In the…
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 customers want to protect their data. They are willing to pay additional…
Decentralized smart contracts enable trustless collaboration but suffer from limited privacy and scalability, which hinders broader adoption. Trusted Execution Environment (TEE) based off-chain execution frameworks offer a promising…