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Trusted execution environments (TEEs) such as \intelsgx facilitate the secure execution of an application on untrusted machines. Sadly, such environments suffer from serious limitations and performance overheads in terms of writing back…

Enclaves have emerged as a particularly compelling primitive to implement trusted execution environments: strongly isolated sensitive user-mode processes in a largely untrusted software environment. While the threat models employed by…

Large Language Models (LLMs) are increasingly used in circuit design tasks and have typically undergone multiple rounds of training. Both the trained models and their associated training data are considered confidential intellectual…

General-purpose operating systems (GPOS), such as Linux, encompass several million lines of code. Statistically, a larger code base inevitably leads to a higher number of potential vulnerabilities and inherently a more vulnerable system. To…

Integrity is critical for maintaining system security, as it ensures that only genuine software is loaded onto a machine. Although confidential virtual machines (CVMs) function within isolated environments separate from the host, it is…

Cloud service providers are adopting Trusted Execution Environments (TEEs) to provide hardware-guaranteed security to applications running on remote, untrusted data centers. However, migrating such applications still relies on the…

Container runtimes provide a stable operational interface for deploying, monitoring, and controlling modern workloads, while trusted execution environments (TEEs) provide hardware-enforced isolation for sensitive computation. Existing…

As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which…

Protecting sensitive information in data-driven collaborations, such as AI training, while meeting the diverse requirements of multiple mutually distrusted stakeholders, is both crucial and challenging. This paper presents Styx, a novel…

Isolation is a long-standing challenge of software security. Traditional privilege rings and virtual memory are more and more augmented with concepts such as capabilities, protection keys, and powerful enclaves. At the same time, we are…

Many safety-critical systems require timely processing of sensor inputs to avoid potential safety hazards. Additionally, to support useful application features, such systems increasingly have a large rich operating system (OS) at the cost…

Trusted Execution Environments (TEEs) are a feature of modern central processing units (CPUs) that aim to provide a high assurance, isolated environment in which to run workloads that demand both confidentiality and integrity. Hardware and…

Intel SGX (Software Guard Extension) is a promising TEE (trusted execution environment) technique that can protect programs running in user space from being maliciously accessed by the host operating system. Although it provides hardware…

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…

Confidential computing is a security paradigm that enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs). By…

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 existing TEEs can truly support CDI computing tasks, as CDI requires high…

This paper presents an approach to provide strong assurance of the secure execution of distributed event-driven applications on shared infrastructures, while relying on a small Trusted Computing Base. We build upon and extend security…

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…

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.…

Authenticated data storage on an untrusted platform is an important computing paradigm for cloud applications ranging from big-data outsourcing, to cryptocurrency and certificate transparency log. These modern applications increasingly…