TZ4Fabric: Executing Smart Contracts with ARM TrustZone : (Practical Experience Report)

Müller, Christina; Brandenburger, Marcus; Cachin, Christian; Felber, Pascal; Göttel, Christian; Schiavoni, Valerio

doi:10.1109/srds51746.2020.00011

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…as part of an access request by the attester node [23]). This ability is notably important in the context of smart contracts that are to be executed within Trusted Execution Environments (TEE), such as SGX [15,50] and ARM Trustzone [53]. Although the technical details are beyond the scope of the current discussion, here the Attestation Evidence must include proof that a specific smart contract (i.e.…”

Section: Attestation Of Nodes In Blockchain Networkmentioning

confidence: 99%

Towards an attestation architecture for blockchain networks

Hardjono

Smith

2021

World Wide Web

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If blockchain networks are to become the building blocks of the infrastructure for the future digital economy, then several challenges related to the resiliency and survivability of blockchain networks need to be addressed. The survivability of a blockchain network is influenced by the diversity of its nodes. Trustworthy device-level attestations permits nodes in a blockchain network to provide truthful evidence regarding their current configuration, operational state, keying material and other system attributes. In the current work we review the recent developments towards a standard attestation architecture and evidence conveyance protocols. We explore the applicability and benefits of a standard attestation architecture to blockchain networks. Finally, we discuss a number of open challenges related to node attestations that has arisen due to changing model of blockchain network deployments, such as the use of virtualization and containerization technologies for nodes in cloud infrastructures.

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Section: Attestation Of Nodes In Blockchain Networkmentioning

confidence: 99%

Towards an attestation architecture for blockchain networks

Hardjono

Smith

2021

World Wide Web

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“…ARM provides a TEE technique called ARM TrustZone (TrustZone) [10]. Because of these points, some researchers worked on providing confidential smart contracts using TrustZone [11], [12].…”

Section: Introductionmentioning

confidence: 99%

“…Especially, TZ4Fabric [12] ensures the confidentiality of smart contracts and execution states in Hyperledger Fabric [13], by executing smart contracts within TrustZone. Although the above work successfully provides confidentiality about smart contracts using TEE, it introduces potential risks because of bloating the code size of TEE.…”

Section: Introductionmentioning

confidence: 99%

“…Although the above work successfully provides confidentiality about smart contracts using TEE, it introduces potential risks because of bloating the code size of TEE. Moreover, previous work [12] uses memory-unsafe languages such as C/C++. However the TrustZone provides a single secure OS in TEE, when the privilege escalation attack occurs to an application within TEE, it can have a detrimental impact not only on secure OS but also on the other applications [14].…”

Section: Introductionmentioning

confidence: 99%

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MECAT: Memory-Safe Smart Contracts in ARM TrustZone

Park,

Kang,

Han

et al. 2024

IEEE Access

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A smart contract is a program executed on a blockchain. However, once the smart contract is deployed on the blockchain, it becomes visible to all participants and remains immutable. Thus, any sensitive information or vulnerabilities in smart contracts can be exposed to potential attackers. To protect the confidentiality of smart contracts, existing studies execute smart contracts in a trusted execution environment (TEE). However, they still suffer from vulnerabilities in smart contracts and potential memory-vulnerability problems. If an attack such as privilege escalation occurs by exploiting this vulnerability, the TEE can have a detrimental effect on the entire system as it has the most privileges in the system. To mitigate the memory vulnerability of the smart contracts in TEE, we propose MECAT, a prototype for memory-safe confidential smart contracts. In essence, MECAT runs smart contracts written in Rust, a memory-safe language, in ARM TrustZone. And MECAT is developed as a software library, allowing developers to easily apply MECAT to their smart contracts. According to our evaluation, MECAT only incurs a 1.36x performance overhead and 0.3% power overhead in single-node environments and can process the 16 clients concurrently in the network made with 8 peer nodes.

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“…Besides of the work presented here, the LEGaTO project produced a number of relevant scientific contributions. TZ4Fabric [108], for instance, is an extension of Hyperledger Fabric to leverage ARM TrustZone for the secure execution of smart contracts. Hermes [109] is an application-level protocol for the dataplane that can operate using generalized deduplication as well as classic deduplication.…”

Section: Legato Projectmentioning

confidence: 99%

Towards energy efficiency by leveraging heterogeneity of clusters and applications

Rocha¹

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The development of applications is becoming every day more diverse in terms of requirements and objectives. Considering this, major cloud providers offer a large variety of hardware types to better fit their customers’ needs. Moreover, a common approach is to design applications in a distributed setup. This means that applications typically consist of building blocks performing individual tasks and communicating with each other to achieve a given objective. Similarly, in order to fulfill the overall requirements of the application, each of its building blocks could feature different instances (e.g., GPUs and FPGAs) as their individual requirements might differ from each other. In this work, we add energy efficiency on top of the multiple objectives that an application might have such as performance or security. Recent studies estimate the already alarming and still increasing energy consumption in the cloud computing sector, especially coming from data centers. We aim at tackling this problem from two different perspectives: 1) cloud providers, and 2) application developers. Looking from the cloud providers point of view, we start by proposing a novel scheduling strategy focused on single-tenant scenarios which leverages the heterogeneity of both clusters and applications to better address the trade-off between performance and energy efficiency. Then, we extend this approach to the multi-tenant scenario where we rely on differential approximation and sprinting to avoid job eviction and resource waste leading to an overall energy gain. In the second part of this thesis, we switch the view from cloud providers to the developers of applications. Here we show that peculiar characteristics of applications can also be leveraged to achieve more energy efficient approaches while maintaining or even further improving their remaining objectives. For that end, we use the automatization of parameter tuning for deep learning applications as use case. We start by characterizing such applications to motivate how relevant the tuning approach taken can be on the end results for tuning, training, and inference. After that, we propose a novel auto tuning approach which takes advantage of the high parallelism and recurring characteristics of such application to simultaneously consider performance, accuracy, and energy efficiency. Once the tuning process is complete, the final model is typically deployed to be used for inference. Therefore, our next step is to extend our approach to perform inference-aware tuning. With this complementary step, we additionally output to the users recommendations on how to deploy their models such that inference-related objectives are also achieved. Finally, independent of the perspective taken, security is one of the major concerns in this area. Cloud providers need to ensure data privacy to their customers, in particular for multi-tenant scenarios where resources are shared among several users. On the other hand, applications related to deep learning are often trained on user-sensitive data which also has to be protected. Recently, trusted executed environments (TEEs) became largely available in server machines as a mechanism to guarantee security. However, such approaches still have their limitations when it comes to usability and the overhead which could be added to applications. Considering this, we close this thesis by proposing an approach which can automate the process of ensuring security in the context of heterogeneous applications and clusters. In summary, this thesis aims at achieving more energy efficient computing while respecting other application requirements such as performance, security, or accuracy. We propose new mechanisms that can be generally applied during the management of jobs as well as optimizations which are specific to a deep learning use case application. Finally, we conclude this work by exploring a TEE based strategy for adding a security layer on top of the proposed approaches.

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TZ4Fabric: Executing Smart Contracts with ARM TrustZone : (Practical Experience Report)

Cited by 10 publications

References 20 publications

Towards an attestation architecture for blockchain networks

Towards an attestation architecture for blockchain networks

MECAT: Memory-Safe Smart Contracts in ARM TrustZone

Towards energy efficiency by leveraging heterogeneity of clusters and applications

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