Trusted Execution Environment Research Articles

Flash controller-based secure execution environment for protecting code confidentiality

With the rapid evolution of Internet-of-Things (IoT), billions of IoT devices have connected to the Internet, collecting information via tags and sensors. For an IoT device, the application code itself and data collected by sensors can be of great commercial value. It is challenging to protect them because IoT devices are prone to compromise due to the inevitable vulnerabilities of commodity Operating Systems. Trusted Execution Environment (TEE) is one of the solutions that protects sensitive data by running security-sensitive workloads in a secure world. However, this solution does not work for most of the IoT devices that are limited in resources.In this paper, we propose Flash Controller-based Secure Execution Environment (FCSEE), an approach to protect security-sensitive code and data for IoT devices using the flash controller. Our approach constructs a secure execution environment on the target flash memory by modifying the execution logic of its controller, leveraging it as a co-processor to execute security-sensitive workloads of the host device. By extending the original functionality of the flash firmware, FCSEE also provides several much-needed security primitives to protect sensitive data. We constructed a prototype based on a Trans-Flash (TF) card and implemented a proof of its confidentiality. Our evaluation results indicate that FCSEE can confidentially execute security-sensitive workloads from the host and efficiently protect its sensitive data.

Relay network-based cross-chain data interaction protocol with integrity audit

Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. However, achieving secure and stable cross-chain data transactions remains a significant challenge. Traditional frameworks often prioritize efficiency, leading to potential security vulnerabilities, such as single points of failure and excessive computational burdens. These issues compromise the reliability of cross-chain interactions, necessitating the development of more robust methodologies to ensure secure data communication across consortium blockchains. To address these concerns, this paper proposes a novel cross-chain data interaction protocol based on a relay network, incorporating auditing and result arbitration mechanisms using short signatures from bilinear pairings. The protocol employs a trusted execution environment (TEE) within the relay network to execute service nodes that record transaction states. This approach not only reduces the computational load on the chain but also facilitates effective coordination for cross-chain consensus. Moreover, we present a cross-chain asynchronous consensus mechanism, named d-PBFT, to improve the flexibility of the cross-chain network. Security analysis and experimental results show that the protocol enhances the efficiency and ensures the security of cross-chain interaction, while reducing on-chain storage consumption. Thus, the protocol has a high level of security and feasibility for cross-chain data interaction among consortium blockchains. © 2014 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.

Survey of research on confidential computing

AbstractAs the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future.

Sort-then-insert: A space efficient and oblivious model aggregation algorithm for top-k sparsification in federated learning

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models while preserving the model privacy of the clients. However, when generating a global model during the aggregation process, a malicious FL server could derive clients’ local model weights. Such a threat cannot be completely eliminated, even if model aggregation is performed in the Trusted Execution Environment of the server, due to memory access pattern attacks. To tackle this challenge, our paper focuses on the top-k model aggregation algorithm in FL and introduces a space efficient and oblivious sparsified model aggregation algorithm named Sort-Then-Insert (STI), which removes the dependency of the memory access pattern on the model input, thus protecting the confidentiality of clients’ models. Compared with Path ORAM, STI is over 100 times faster, and compared with the state-of-the-art solution for the same problem, Olive, our theoretical analysis and experiments demonstrate that STI can achieve comparable performance overhead (O(nklog2(nk))+O(dlog2(d)) for STI vs. O((nk+d)log2(nk+d)) for Olive) and reduced space overhead (max(O(nk),O(d)) for STI vs. O(nk+d) for Olive).

An SGX-based online voting protocol with maximum voter privacy

Electronic voting (E-voting), a crucial method in modern society, balances efficiency with the need for equity, reliability, and privacy to accurately record votes and maintain democracy. However, there is the challenge of potential leakage of voting information in scenarios where administrators of voting system may act maliciously. Moreover, contemporary collaborative counting methods face a significant increase in the cost of storage and heightened communication overhead in large-scale e-voting. In response, this paper proposes an innovative online voting protocol that integrates Intel SGX, homomorphic encryption, and zero-knowledge proof. This protocol aims to cut down the cost of communication and storage while maximizing the protection of voter privacy. The protocol relies on SGX to provide a trusted execution environment for the voting process, guarding against malicious attacks from external software or high-privileged administrators. In the proposed protocol, voters apply homomorphic encryption algorithms to locally encrypt their ballots, no longer relying on other entities (such as the voting system or SGX) for encryption. Then the encrypted ballots are submitted to SGX to mitigate the threat of data leakage in SGX. Through the adoption of zero-knowledge proof technology, the voting protocol can verify the legitimacy of votes without revealing their content. The proposed solution introduces the SGXVOT architecture, comprising two enclaves – Enclave V responsible for tallying individual encrypted votes, and Enclave T responsible for decrypting and publishing voting results. This design ensures that individual voters’ ballots remain in ciphertext, preserving the confidentiality of votes and voter privacy. Additionally, the protocol incorporates a “One-Time Pad” encryption communication protocol to guarantee the confidentiality of communication messages between Enclave V and Enclave T. The thorough security analysis and performance evaluation demonstrate the superior performance of the proposed solution in terms of security, practicality, and scalability.

Privacy preserving and secure robust federated learning: A survey

SummaryFederated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.

Secure Multiparty Computation Using Secure Virtual Machines

The development of new processor capabilities which enable hardware-based memory encryption, capable of isolating and encrypting application code and data in memory, have led to the rise of confidential computing techniques that protect data when processed on untrusted computing resources (e.g., cloud). Before confidential computing technologies, applications that needed data-in-use protection, like outsourced or secure multiparty computation, used purely cryptographic techniques, which had a large negative impact on the processing performance. Processing data in trusted enclaves protected by confidential computing technologies promises to protect data-in-use while possessing a negligible performance penalty. In this paper, we have analyzed the state-of-the-art in the field of confidential computing and present a Confidential Computing System for Artificial Intelligence (CoCoS.ai), a system for secure multiparty computation, which uses virtual machine-based trusted execution environments (in this case, AMD Secure Encrypted Virtualization (SEV)). The security of the proposed solution, as well as its performance, have been formally analyzed and measured. The paper reveals many gaps not reported previously that still exist in the current confidential computing solutions for the secure multiparty computation use case, especially in the processes of creating new secure virtual machines and their attestation, which are tailored for single-user use cases.

Penetralium: Privacy-preserving and memory-efficient neural network inference at the edge

The proliferation of artificial intelligence and edge computing has led to an increase in the deployment of proprietary deep learning models on third-party edge servers or devices to power mission-critical applications. However, this trend raises concerns about model privacy, particularly on untrusted edge platforms. Protecting model privacy in such scenarios requires addressing challenges such as untrustworthy model deployment environments, resource-constrained Trusted Execution Environments (TEE), and vulnerability to privacy inference attacks. To address these challenges, this paper proposes Penetralium, a system-algorithm jointly optimized model inference system on edge computing platforms. Penetralium runs models in the TEE by building an underlying computational engine. We propose an adaptive decomposition algorithm that builds a computing pipeline for models, which adapts to the underlying trusted components. Additionally, Penetralium uses a lightweight confidence score perturbation policy to protect against advanced privacy inference attacks on deep learning models. Experimental results demonstrate that Penetralium provides strong security guarantees with reasonable performance. The system not only reduces inference latency and memory consumption overhead but also improves the overall robustness of the system against advanced attacks.

Exploiting RPMB authentication in a closed source TEE implementation

Embedded Multimedia Cards (eMMCs) provide a protected memory area called the Replay Protected Memory Block (RPMB). eMMCs are commonly used as storage media in modern smartphones. In order to protect these devices from unauthorized access, important data is stored in the RPMB area in an authenticated manner. Modification of the RPMB data requires a pre-shared authentication key. An unauthorized user cannot change the stored data.On modern devices, this pre-shared key is generated and used exclusively within a Trusted Execution Environment (TEE) preventing attackers from access. In this paper, we investigate how the authentication key for RPMB is programmed on the eMMC. We found that this key can be extracted directly from the target memory chip. Once obtained, the authentication key can be used to manipulate stored data. In addition, poor implementation of certain security features, aimed at preventing replay attacks using RPMB on the host system can be broken by an attacker. We show how the authentication key can be extracted and how it can be used to break the anti-rollback protection to enable data restoration even after a data wipe operation has been completed.Our findings show that non-secure RPMB implementations can enable forensic investigators to break security features implemented on modern smartphones.

Profiling with trust: system monitoring from trusted execution environments

AbstractLarge-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.

Hardware-hardened Sandbox Enclaves for Trusted Serverless Computing

In cloud-based serverless computing, an application consists of multiple functions provided by mutually distrusting parties. For secure serverless computing, the hardware-based trusted execution environment (TEE) can provide strong isolation among functions. However, not only protecting each function from the host OS and other functions, but also protecting the host system from the functions, is critical for the security of the cloud servers. Such an emerging trusted serverless computing poses new challenges: Each TEE must be isolated from the host system bi-directionally, and the system calls from it must be validated. In addition, the resource utilization of each TEE must be accountable in a mutually trusted way. However, the current TEE model cannot efficiently represent such trusted serverless applications. To overcome the lack of such hardware support, this article proposes an extended TEE model called Cloister , designed for trusted serverless computing. Cloister proposes four new key techniques. First, it extends the hardware-based memory isolation in SGX to confine a deployed function only within its TEE (enclave). Second, it proposes a trusted monitor enclave that filters and validates system calls from enclaves. Third, it provides a trusted resource accounting mechanism for enclaves that is agreeable to both service developers and cloud providers. Finally, Cloister accelerates enclave loading by redesigning its memory verification for fast function deployment. Using an emulated Intel SGX platform with the proposed extensions, this article shows that trusted serverless applications can be effectively supported with small changes in the SGX hardware.

Design and evaluation of architectural framework for a secured local energy market model based on distributed ledger technologies

AbstractBlockchain‐based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper‐proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on‐chain features of blockchain with trusted execution environments to develop a transparent, tamper‐resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

A Blockchain-Based Privacy-Preserving Healthcare Data Sharing Scheme for Incremental Updates

With the rapid development of artificial intelligence (AI) in the healthcare industry, the sharing of personal healthcare data plays an essential role in advancing medical AI. Unfortunately, personal healthcare data sharing is plagued by challenges like ambiguous data ownership and privacy leakage. Blockchain, which stores the hash of shared data on-chain and ciphertext off-chain, is treated as a promising approach to address the above issues. However, this approach lacks a flexible and reliable mechanism for incremental updates of the same case data. To avoid the overhead of authentication, access control, and rewards caused by on-chain data changes, we propose a blockchain and trusted execution environment (TEE)-based privacy-preserving sharing scheme for healthcare data that supports incremental updates. Based on chameleon hash and TEE, the scheme achieves reliable incremental updates and verification without changing the on-chain data. In the scheme, for privacy concerns, off-chain data are protected through symmetric encryption, whereas data verification, decryption, and computation are performed within TEE. The experimental results show the feasibility and effectiveness of the proposed scheme.

Blockchain based solutions for privacy-preserving authentication and authorization in networks

Securing sensitive user information and maintaining privacy during authentication and authorization processes is crucial in today’s interconnected digital world. This study investigates new approaches to tackle these obstacles by combining blockchain technology with Trusted Execution Environment (TEE). The proposed system introduces a new method that combines the advantages of both technologies to create a strong and privacy-focused framework for network security. The study assesses the effectiveness of the suggested system based on key parameters, focusing specifically on Transaction Throughput (TPS) and Latency. The combination of blockchain and TEE aims to improve privacy protection and security protocols, providing a dependable solution for privacy-focused authentication and authorization in network settings. The experiments confirmed the efficacy of the proposed model. The Transaction Throughput (TPS) reached 296 transactions per second, demonstrating the system’s scalability and capacity to manage a large number of transactions. The Latency was measured at 54 milliseconds, indicating a fast and responsive authentication and authorization process. The research enhances the development of privacy-preserving solutions by emphasizing the importance of the blockchain and TEE collaboration. The proposed model surpasses performance expectations set by traditional methods, positioning itself as a significant advancement in network security. The results highlight the system’s ability to significantly improve privacy protection in digital transactions and set the stage for advancements in secure, efficient, and privacy-focused authentication and authorization systems. between the quantity demand and the price.

SGXFault: An Efficient Page Fault Handling Mechanism for SGX Enclaves

Trusted Execution Environments (TEEs) such as Intel SGX are becoming a commonplace for the security in server processors. Intel SGX provides the guarantees of confidentiality, integrity and replay attack protection for a specific region of memory. However, the downside of it is the performance bottleneck due to the context switching overhead and the page faults for the larger memory footprint applications. In this paper, we propose a scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGXFault</i> which ensures for major part of the execution, the pages are available in the secure memory when needed. We do this decision at the last level cache (LLC) by locking the blocks of the frequent pages to the LLC using Cache Lockdown mechanism. In addition to this, we do a page-level prediction based prefetching when there is a miss in the LLC. Using the combination of both the approaches, we are able to outperform baseline Intel SGX and recent competing scheme by around 18.6% and 17.8% respectively.

CyFence: Securing Cyber-physical Controllers Via Trusted Execution Environment

CyFence: Securing Cyber-physical Controllers Via Trusted Execution Environment

VeriTrain: Validating MLaaS Training Efforts via Anomaly Detection

Machine learning as a service (MLaaS) offers users the benefit of training state-of-the-art neural network models on fast hardware with low costs. However, it also brings security concerns since the user does not fully trust the cloud. To prove to the user that the ML training results are legitimate, existing approaches mainly adopt cryptographic techniques such as secure multi-party computation, which incur large overheads. In this paper, we model the problem of verifying ML training efforts as an anomaly detection problem. We design a verification system, dubbed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> , which combines unsupervised anomaly detection approaches and hypothesis testing techniques to verify the legitimacy of training efforts on the MLaaS cloud. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> is run inside trusted execution environments (TEEs) on the same cloud machine to ensure the integrity of its execution. We consider a threat model where the cloud model trainer is a lazy attacker and tries to fool <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> with minimum training effort. We perform extensive evaluations on multiple neural network models and datasets, which shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> performs well in detecting parameter updates crafted by the attacker. We also implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> with Intel SGX and show that it only incurs moderate overheads.

CacheIEE: Cache-Assisted Isolated Execution Environment on ARM Multi-Core Platforms

ARM TrustZone technology has been widely used to create Trusted Execution Environments (TEEs) for enhancing the security of applications. However, the increasing number of installed security-sensitive applications in the secure world will inevitably enlarge the trusted computing base (TCB) of TEE systems. To minimize the TCB of the secure world and increase application portability, Isolated Execution Environments (IEEs) are proposed to protect applications in enclaves created in the normal world. However, existing IEE systems cannot provide the same level of security as the TEE systems, particularly, on resolving the multi-vector attacks that include both physical memory disclosure attacks and software attacks. In this paper, we develop a new cache-assisted IEE system called CacheIEE that creates enclaves in the L1 data cache of the normal world to protect sensitive data against multi-vector attacks. First, by always storing the sensitive data in the L1 data cache, CacheIEE can effectively prevent physical memory disclosure attacks. Second, we protect the L1 data cache against untrusted rich OS running in other cores. To support more applications, CacheIEE can process large-size sensitive data in the L1 data cache with constrained capacity. We implement a system prototype of CacheIEE and verify its security and practicability.

Janus: A Trusted Execution Environment Approach for Attack Detection in Industrial Robot Controllers

Janus: A Trusted Execution Environment Approach for Attack Detection in Industrial Robot Controllers

Building a Lightweight Trusted Execution Environment for Arm GPUs

Building a Lightweight Trusted Execution Environment for Arm GPUs