An Exploratory Study of Attestation Mechanisms for Trusted Execution Environments

Ménétrey, Jämes; Göttel, Christian; Pasin, Marcelo; Felber, Pascal; Schiavoni, Valerio

doi:10.48550/arxiv.2204.06790

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Trusted execution environment (TEE) uses the method of isolating some of the hardware and software resources to build a secure area on a computing device to ensure the protection of sensitive data and operations [26].…”

Section: Trusted Execution Environmentmentioning

confidence: 99%

Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption

Zhang,

Lu,

Qiu

et al. 2023

Entropy

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Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.

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Section: Trusted Execution Environmentmentioning

confidence: 99%

Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption

Zhang,

Lu,

Qiu

et al. 2023

Entropy

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“…AtQ is an opaque data structure signed by a Quoting Service (QService) with an attestation key (AK), and it can be called a quote [16] or evidence, which is used to establish trustworthiness through identities. Because of this, the quote encapsulates code identity, ISV identity, TEE identity, and various security attributes, e.g., security version number (SVN) of a TEE [10], asso-ciated with a TService instance.…”

Section: Attest Quote (Atq)mentioning

confidence: 99%

HTTPA/2: a Trusted End-to-End Protocol for Web Services

King¹,

Wang²

2022

Preprint

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We received positive feedback and inquiries on the previous version of HTTPA [11] (HTTPA/1). As a result, we present a major revision of the HTTPA protocol (HTTPA/2) to protect sensitive data in HTTPA transactions from cyber attacks. Comparatively, the previous work [11] is mainly focused on how to include remote attestation (RA) and secret provisioning to the HTTP protocol with Transport Layer Security (TLS) protection across the Internet, which is great, but it comes at a price. In contrast, HTTPA/2 is not necessary to rely on the TLS protocol, such as TLS 1.3 [20], for secure communication over the Internet. The design of HTTPA/2 follows the SIGMA model [13] to establish a trusted (attested) and secure communication context between endpoints at layer 7 (L7) of the OSI model. Different from connection-based protocols, HTTPA/2 is transaction-based in which the TEE is considered to be a new type of requested resource over the Internet. In addition to protecting sensitive data transmitted to TEE-based services (TServices), HTTPA/2 can potentially be used to optimize the end-to-end performance of Internet or cloud backend traffic, thus saving energy and reducing the operational costs of Cloud Service Providers (CSPs). We envision that HTTPA/2 will further enable confidential web services and trustworthy AI applications in the future.

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