Nowadays, there is a trend to design complex, yet secure systems. In this context, the Trusted Execution Environment (TEE) was designed to enrich the previously defined trusted platforms. TEE is commonly known as an isolated processing environment in which applications can be securely executed irrespective of the rest of the system. However, TEE still lacks a precise definition as well as representative building blocks that systematize its design. Existing definitions of TEE are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related concepts, such as secure execution environment (SEE). In this paper, we propose a precise definition of TEE and analyze its core properties. Furthermore, we discuss important concepts related to TEE, such as trust and formal verification. We give a short survey on the existing academic and industrial ARM TrustZone-based TEE, and compare them using our proposed definition. Finally, we discuss some known attacks on deployed TEE as well as its wide use to guarantee security in diverse applications.

https://hal.archives-ouvertes.fr/hal-01246364/document

Trusted Execution Environment: What It is, and What It is Not

The surging interest in blockchain technology has revitalized the search for effective Byzantine consensus schemes. In particular, the blockchain community has been looking for ways to effectively integrate traditional Byzantine fault-tolerant (BFT) protocols into a blockchain consensus layer allowing various financial institutions to securely agree on the order of transactions. However, existing BFT protocols can only scale to tens of nodes due to their $O(n^2)$ message complexity. In this paper, we propose FastBFT, a fast and scalable BFT protocol. At the heart of FastBFT is a novel message aggregation technique that combines hardware-based trusted execution environments (TEEs) with lightweight secret sharing. Combining this technique with several other optimizations (i.e., optimistic execution, tree topology and failure detection), FastBFT achieves low latency and high throughput even for large scale networks. Via systematic analysis and experiments, we demonstrate that FastBFT has better scalability and performance than previous BFT protocols.

/pdf/scalable-byzantine-consensus-via-hardware-assisted-secret-2y7ns85p7u.pdf

Scalable Byzantine Consensus via Hardware-Assisted Secret Sharing

Intel SGX promises powerful security: an arbitrary number of user-mode enclaves protected against physical attacks and privileged software adversaries. However, to achieve this, Intel extended the x86 architecture with an isolation mechanism approaching the complexity of an OS microkernel, implemented by an inscrutable mix of silicon and microcode. While hardware-based security can offer performance and features that are difficult or impossible to achieve in pure software, hardware-only solutions are difficult to update, either to patch security flaws or introduce new features. Komodo illustrates an alternative approach to attested, on-demand, user-mode, concurrent isolated execution. We decouple the core hardware mechanisms such as memory encryption, address-space isolation and attestation from the management thereof, which Komodo delegates to a privileged software monitor that in turn implements enclaves. The monitor's correctness is ensured by a machine-checkable proof of both functional correctness and high-level security properties of enclave integrity and confidentiality. We show that the approach is practical and performant with a concrete implementation of a prototype in verified assembly code on ARM TrustZone. Our ultimate goal is to achieve security equivalent to or better than SGX while enabling deployment of new enclave features independently of CPU upgrades. The Komodo specification, prototype implementation, and proofs are available at https://github.com/Microsoft/Komodo.

/pdf/komodo-using-verification-to-disentangle-secure-enclave-1l15c5dr3p.pdf

Komodo: Using verification to disentangle secure-enclave hardware from software

Developments in technologies, attitudes and investment are transforming the space environment, achieving greater accessibility for an increasing number of parties. New and proposed constellations will increase the in-orbit satellite population by the order of thousands, expanding the threat landscape of the space industry. This article analyses past satellite security threats and incidents to assess the motivations and characteristics of adversarial threats to satellites. The ground and radio frequency communications were the most favoured targets; however, the boom of satellites constellations in the upcoming years may shift this focus towards the space segment which must be addressed. Key technology advancements and open issues in the satellite industry related to security and operational requirements are also discussed.

/pdf/cyber-security-in-new-space-3vdh6cdfv4.pdf

Cyber security in New Space

Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.

Efficient Deep Learning on Multi-Source Private Data.

A “Firmware-Based TPM” or “fTPM” ensures that secure code execution is isolated to prevent a wide variety of potential security breaches. Unlike a conventional hardware based Trusted Platform Module (TPM), isolation is achieved without the use of dedicated security processor hardware or silicon. In general, the fTPM is first instantiated in a pre-OS boot environment by reading the fTPM from system firmware or firmware accessible memory or storage and placed into read-only protected memory of the device. Once instantiated, the fTPM enables execution isolation for ensuring secure code execution. More specifically, the fTPM is placed into protected read-only memory to enable the device to use hardware such as the ARM® architecture's TrustZone™ extensions and security primitives (or similar processor architectures), and thus the devices based on such architectures, to provide secure execution isolation within a “firmware-based TPM” without requiring hardware modifications to existing devices.

Firmware-based trusted platform module for arm processor architectures and trustzone security extensions

Commodity CPU architectures, such as ARM and Intel CPUs, have started to offer trusted computing features in their CPUs aimed at displacing dedicated trusted hardware. Unfortunately, these CPU architectures raise serious challenges to building trusted systems because they omit providing secure resources outside the CPU perimeter. This paper shows how to overcome these challenges to build software systems with security guarantees similar to those of dedicated trusted hardware. We present the design and implementation of a firmware-based TPM 2.0 (fTPM) leveraging ARM TrustZone. Our fTPM is the reference implementation of a TPM 2.0 used in millions of mobile devices. We also describe a set of mechanisms needed for the fTPM that can be useful for building more sophisticated trusted applications beyond just a TPM.

/pdf/ftpm-a-software-only-implementation-of-a-tpm-chip-gmhs70s05r.pdf

fTPM: A Software-Only Implementation of a {TPM} Chip

The Internet of Things (IoT) is rapidly emerging as one of the dominant computing paradigms of this decade. Applications range from in-home entertainment to large-scale industrial deployments such as controlling assembly lines and monitoring traffic. While IoT devices are in many respects similar to traditional computers, user expectations and deployment scenarios as well as cost and hardware constraints are sufficiently different to create new security challenges as well as new opportunities. This is especially true for large-scale IoT deployments in which a central entity deploys and controls a large number of IoT devices with minimal human interaction. Like traditional computers, IoT devices are subject to attack and compromise. Large IoT deployments consisting of many nearly identical devices are especially attractive targets. At the same time, recovery from root compromise by conventional means becomes costly and slow, even more so if the devices are dispersed over a large geographical area. In the worst case, technicians have to travel to all devices and manually recover them. Data center solutions such as the Intelligent Platform Management Interface (IPMI) which rely on separate service processors and network connections are not only not supported by existing IoT hardware, but are unlikely to be in the foreseeable future due to the cost constraints of mainstream IoT devices. This paper presents Cider, a system that can recover IoT devices within a short amount of time, even if attackers have taken root control of every device in a large deployment. The recovery requires minimal manual intervention. After the administrator has identified the compromise and produced an updated firmware image, he/she can instruct Cider to force the devices to reset and to install the patched firmware on the devices. We demonstrate the universality and practicality of Cider by implementing it on three popular IoT platforms (HummingBoard Edge, Raspberry Pi Compute Module 3 and Nucleo-L476RG) spanning the range from high to low end. Our evaluation shows that the performance overhead of Cider is generally negligible.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/19skggDcR0s/pdf

Dominance as a New Trusted Computing Primitive for the Internet of Things

This paper presents the design and implementation of a firmware-based TPM 2.0 (fTPM) leveraging ARM TrustZone. The fTPM is the reference implementation used in millions of mobile devices, and was the first hardware or software implementation to support the newly released TPM 2.0 specification. This paper describes the shortcomings of ARM’s TrustZone for implementing secure service (such as our implementation), and presents three different approaches to overcome them. Additionally, the paper analyzes the fTPM’s security guarantees and demonstrates that many of the ARM TrustZone’s shortcomings remain present in future trusted hardware, such as Intel’s Software Guard Extensions (SGX).

/pdf/ftpm-a-firmware-based-tpm-2-0-implementation-3u1c2u0dbg.pdf

fTPM: A Firmware-based TPM 2.0 Implementation

1 Abstract RIoT (Robust Internet-of-Things) is an architecture for providing foundational trust services to computing devices. The trust services include device identity, sealing, attestation, and data integrity. The term “Robust” is used because the minimal trusted computing base is tiny, and because RIoT capabilities can remotely re-establish trust in devices that have been compromised by malware. The term IoT is used because these services can be provided at low cost on even the tiniest of devices.

/pdf/riot-a-foundation-for-trust-in-the-internet-of-things-ladu4fit9n.pdf

RIoT - A Foundation for Trust in the Internet of Things

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