相关论文: Using Trusted Execution Environments for Secure St…

Confidential multi-stakeholder machine learning (ML) allows multiple parties to perform collaborative data analytics while not revealing their intellectual property, such as ML source code, model, or datasets. State-of-the-art solutions…

The growing availability of hardware-based trusted execution environments (TEEs) in commodity processors has recently advanced support (i.e., design, implementation and deployment frameworks) for network-based secure services. Examples of…

Intel SGX is known to be vulnerable to a class of practical attacks exploiting memory access pattern side-channels, notably page-fault attacks and cache timing attacks. A promising hardening scheme is to wrap applications in hardware…

Decision support systems are increasingly adopted to automate decision-making processes across industries, organizations, and governments. Decision support demands data privacy, integrity, and availability while ensuring customization,…

Privacy concerns in outsourced cloud databases have become more and more important recently and many efficient and scalable query processing methods over encrypted data have been proposed. However, there is very limited work on how to…

We present PSL, a lightweight, secure and stateful Function-as-a-Serivce (FaaS) framework for Trusted Execution Environments (TEEs). The framework provides rich programming language support on heterogeneous TEE hardware for statically…

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…

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…

The ever-rising computation demand is forcing the move from the CPU to heterogeneous specialized hardware, which is readily available across modern datacenters through disaggregated infrastructure. On the other hand, trusted execution…

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…

Cloud computing offers the economies of scale for computational resources with the ease of management, elasticity, and fault tolerance. To take advantage of these benefits, many enterprises are contemplating to outsource the middlebox…

The abundance and rich varieties of data are enabling many transformative applications of big data analytics that have profound societal impacts. However, there are also increasing concerns regarding the improper use of individual data…

Fully homomorphic encryption (FHE) and trusted execution environments (TEE) are two approaches to provide confidentiality during data processing. Each approach has its own strengths and weaknesses. In certain scenarios, computations can be…

The need for data trading promotes the emergence of data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. A dishonest centralized trading…

Trusted Execution Environments (TEEs) have emerged at the forefront of edge computing to combat the lack of trust between system components. Field Programmable Gate Arrays (FPGAs) are commonly used as edge computers but were not created…

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…

We provide enhanced security against insider attacks in services that manage extremely sensitive data. One example is a #MeToo use case where sexual harassment complaints are reported but only revealed when another complaint is filed…

With the popularity of cloud computing and machine learning, it has been a trend to outsource machine learning processes (including model training and model-based inference) to cloud. By the outsourcing, other than utilizing the extensive…

Wide deployment of machine learning models on edge devices has rendered the model intellectual property (IP) and data privacy vulnerable. We propose GNNVault, the first secure Graph Neural Network (GNN) deployment strategy based on Trusted…

Smart devices produce security-sensitive data and keep them in on-device storage for persistence. The current storage stack on smart devices, however, offers weak security guarantees: not only because the stack depends on a vulnerable…