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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…

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…

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…

A smart contract on a blockchain cannot keep a secret because its data is replicated on all nodes in a network. To remedy this problem, it has been suggested to combine blockchains with trusted execution environments (TEEs), such as Intel…

With the proliferation of Trusted Execution Environments (TEEs) such as Intel SGX, a number of cloud providers will soon introduce TEE capabilities within their offering (e.g., Microsoft Azure). Although the integration of SGX within the…

Although the importance of using static analysis to detect taint-style vulnerabilities in Linux-based embedded firmware is widely recognized, existing approaches are plagued by three major limitations. (a) Approaches based on symbolic…

Protected-module architectures (PMAs) have been proposed to provide strong isolation guarantees, even on top of a compromised system. Unfortunately, Intel SGX -- the only publicly available high-end PMA -- has been shown to only provide…

This paper presents PUBSUB-SGX, a content-based publish-subscribe system that exploits trusted execution environments (TEEs), such as Intel SGX, to guarantee confidentiality and integrity of data as well as anonymity and privacy of…

MLaaS (Machine Learning as a Service) has become popular in the cloud computing domain, allowing users to leverage cloud resources for running private inference of ML models on their data. However, ensuring user input privacy and secure…

The deployment of large language models (LLMs) on third-party devices requires new ways to protect model intellectual property. While Trusted Execution Environments (TEEs) offer a promising solution, their performance limits can lead to a…

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…

Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis…

Intel's software guard extensions (SGX) provide hardware enclaves to guarantee confidentiality and integrity for sensitive code and data. However, systems leveraging such security mechanisms must often pay high performance overheads. A…

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…

The popularity of the Java programming language has led to its wide adoption in cloud computing infrastructures. However, Java applications running in untrusted clouds are vulnerable to various forms of privileged attacks. The emergence of…

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose…

A number of trusted execution environments (TEEs) have been proposed by both academia and industry. However, most of them require specific hardware or firmware changes and are bound to specific hardware vendors (such as Intel, AMD, ARM, and…

This paper proposes a system, entitled Concealer that allows sharing time-varying spatial data (e.g., as produced by sensors) in encrypted form to an untrusted third-party service provider to provide location-based applications (involving…

Deployed large language models (LLMs) often rely on speculative decoding, a technique that generates and verifies multiple candidate tokens in parallel, to improve throughput and latency. In this work, we reveal a new side-channel whereby…

Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimating the model's accuracy during offline evaluations, possibly…