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We introduce LLA, an effective intellectual property (IP) protection scheme for generative AI models. LLA leverages the synergy between hardware and software to defend against various supply chain threats, including model theft, model…
In recent years, we have witnessed unprecedented growth in using hardware-assisted Trusted Execution Environments (TEE) or enclaves to protect sensitive code and data on commodity devices thanks to new hardware security features, such as…
Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential properties for trustworthy data systems. The rise of cloud computing and the growing demand for multiparty applications however means that…
The Android ecosystem relies on either TrustZone (e.g., OP-TEE, QTEE, Trusty) or trusted hypervisors (pKVM, Gunyah) to isolate security-sensitive services from malicious apps and Android bugs. TrustZone allows any secure world code to…
In the last two decades, the evolving cyber-threat landscape has brought to center stage the contentious tradeoffs between the security and performance of modern microprocessors. The guarantees provided by the hardware to ensure no…
It has been widely accepted that Graphics Processing Units (GPU) is one of promising schemes for encryption acceleration, in particular, the support of complex mathematical calculations such as integer and logical operations makes the…
Large Language Model (LLM) agents provide powerful automation capabilities, but they also create a substantially broader attack surface than traditional applications due to their tight integration with non-deterministic models and…
Confidential Computing has emerged to address data security challenges in cloud-centric deployments by protecting data in use through hardware-level isolation. However, reliance on a single hardware root of trust (RoT) limits user…
NVIDIA GPU Confidential Computing (GPU-CC) aims to provide secure execution for AI workloads. For end users, enabling GPU-CC is seamless and requires no modifications to existing applications. However, this ease of adoption relies on a…
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…
Because FPGAs outperform traditional processing cores like CPUs and GPUs in terms of performance per watt and flexibility, they are being used more and more in cloud and data center applications. There are growing worries about the security…
We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applications using standard high-level programming languages (e.g., C)…
Trusted Execution Environments (TEEs) are used to protect sensitive data and run secure execution for security-critical applications, by providing an environment isolated from the rest of the system. However, over the last few years, TEEs…
Confidential computing has gained prominence due to the escalating volume of data-driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly, across distributed…
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…
As an essential technology underpinning trusted computing, the trusted execution environment (TEE) allows one to launch computation tasks on both on- and off-premises data while assuring confidentiality and integrity. This article provides…
Confidential Computing enhances privacy of data in-use through hardware-based Trusted Execution Environments (TEEs) that use attestation to verify their integrity, authenticity, and certain runtime properties, along with those of the…
Hardware-enclaves that target complex CPU designs compromise both security and performance. Programs have little control over micro-architecture, which leads to side-channel leaks, and then have to be transformed to have worst-case control-…
Unlike other accelerators, FPGAs are capable of supporting cache coherency, thereby turning them into a more powerful architectural option than just a peripheral accelerator. However, most existing deployments of FPGAs are either non-cache…
The integration of clinical data offers significant potential for the development of personalized medicine. However, its use is severely restricted by the General Data Protection Regulation (GDPR), especially for small cohorts with rare…