Related papers: Dstack: A Zero Trust Framework for Confidential Co…
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based, centralized infrastructures. This requires data owners to upload potentially sensitive data to…
Live migration of applications is a fundamental capability for enabling resilient computing in modern distributed systems. However, extending this functionality to trusted applications (TA) -- executing within Trusted Execution Environments…
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…
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 execution environments (TEEs) provide an environment for running workloads in the cloud without having to trust cloud service providers, by offering additional hardware-assisted security guarantees. However, main memory encryption…
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…
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 execution of convolutional layers and the decryption of…
We aim to provide trusted time measurement mechanisms to applications and cloud infrastructure deployed in environments that could harbor potential adversaries, including the hardware infrastructure provider. Despite Trusted Execution…
Enterprise software supply chains are increasingly vulnerable to infrastructure attacks, resulting in financial and reputational damage. Ensuring the integrity and provenance of software artifacts remains a significant challenge, where…
Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability.…
Confidential Virtual Machines (CVMs) provide isolation guarantees for data in use, but their threat model does not include physical level protection and side-channel attacks. Therefore, current deployments rely on trusted cloud providers to…
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…
Trusted Execution Environments (TEEs) are designed to protect the privacy and integrity of data in use. They enable secure data processing and sharing in peer-to-peer networks, such as vehicular ad hoc networks of autonomous vehicles,…
Applications running in Trusted Execution Environments (TEEs) commonly use untrusted external services such as host File System. Adversaries may maliciously alter the normal service behavior to trigger subtle application bugs that would…
Trusted Execution Environments (TEEs) suffer from performance issues when executing certain management instructions, such as creating an enclave, context switching in and out of protected mode, and swapping cached pages. This is especially…
Data-driven intelligent applications in modern online services have become ubiquitous. These applications are usually hosted in the untrusted cloud computing infrastructure. This poses significant security risks since these applications…
Smart contracts are applications that execute on blockchains. Today they manage billions of dollars in value and motivate visionary plans for pervasive blockchain deployment. While smart contracts inherit the availability and other security…
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)…
The big data industry is facing new challenges as concerns about privacy leakage soar. One of the remedies to privacy breach incidents is to encapsulate computations over sensitive data within hardware-assisted Trusted Execution…
Mobile devices rely on Trusted Execution Environments (TEEs) to execute security-critical code and protect sensitive assets. This security-critical code is modularized in components known as Trusted Applications (TAs). Vulnerabilities in…