Trusted execution environment with Intel SGX

Chakrabarti, Somnath; Knauth, Thomas; Kuvaiskii, Dmitrii; Steiner, Michael; Vij, Mona

doi:10.1016/b978-0-12-816197-5.00008-5

Cited by 9 publications

(7 citation statements)

References 1 publication

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“…An appropriately designed mechanism ensures a desired equilibrium when every worker acts rationally and in its own best interest. Such a mechanism has low complexity and is self-organizing, avoiding the need for Trusted Execution Environments (TEE) [19] or secure multi-party computation, yet makes assumptions about the degree of information available.…”

Section: Incentive Mechanismmentioning

confidence: 99%

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

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The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.

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Section: Incentive Mechanismmentioning

confidence: 99%

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

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“…In recent years, Intel deployed SGX technology in their CPUs to help users to build a minimal trusted computing base. It allows users to put sensitive data and codes of the program in a secure container called enclave [18], providing confidentiality and integrity protection. Although SGX technology was once considered the future of trusted execution environments, it fails to solve cache side-channel attacks.…”

Section: Side-channel Attacksmentioning

confidence: 99%

Survey of CPU Cache-Based Side-Channel Attacks: Systematic Analysis, Security Models, and Countermeasures

Zeng

2021

Security and Communication Networks

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Privacy protection is an essential part of information security. The use of shared resources demands more privacy and security protection, especially in cloud computing environments. Side-channel attacks based on CPU cache utilize shared CPU caches within the same physical device to compromise the system’s privacy (encryption keys, program status, etc.). Information is leaked through channels that are not intended to transmit information, jeopardizing system security. These attacks have the characteristics of both high concealment and high risk. Despite the improvement in architecture, which makes it more difficult to launch system intrusion and privacy leakage through traditional methods, side-channel attacks ignore those defenses because of the shared hardware. Difficult to be detected, they are much more dangerous in modern computer systems. Although some researchers focus on the survey of side-channel attacks, their study is limited to cryptographic modules such as Elliptic Curve Cryptosystems. All the discussions are based on real-world applications (e.g., Curve25519), and there is no systematic analysis for the related attack and security model. Firstly, this paper compares different types of cache-based side-channel attacks. Based on the comparison, a security model is proposed. The model describes the attacks from four key aspects, namely, vulnerability, cache type, pattern, and range. Through reviewing the corresponding defense methods, it reveals from which perspective defense strategies are effective for side-channel attacks. Finally, the challenges and research trends of CPU cache-based side-channel attacks in both attacking and defending are explored. The systematic analysis of CPU cache-based side-channel attacks highlights the fact that these attacks are more dangerous than expected. We believe our survey would draw developers’ attention to side-channel attacks and help to reduce the attack surface in the future.

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“…An appropriately designed mechanism ensures a desired equilibrium when every worker acts rationally and in its own best interest. Such a mechanism has low complexity and is self-organizing, avoiding the need for Trusted Execution Environments [7] or cryptographic schemes. In a FL context, this demands a carefully designed reward strategy based on the quality of contributions.…”

Section: Introductionmentioning

confidence: 99%

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

Witt,

Zafar,

Shen

et al. 2021

Preprint

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The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes. This security-perdesign approach in combination with increasingly performant Internet of Things (IoT) and mobile devices opens up a new realm of possibilities to utilize private data from industries as well as from individuals as input for artificial intelligence model training. Yet in previous FL systems, lack of trust due to the imbalance of power between workers and a central authority, the assumption of altruistic worker participation and the inability to correctly measure and compare contributions of workers hinder this technology from scaling beyond small groups of already entrusted entities towards mass adoption. This work aims to mitigate the aforementioned issues by introducing a novel decentralized federated learning framework where heavily compressed 1-bit soft-labels, resembling 1-hot label predictions, are aggregated on a smart contract. In a context where workers' contributions are now easily comparable, we modify the Peer Truth Serum for Crowdsourcing mechanism (PTSC) for FD to reward honest participation based on peer consistency in an incentive compatible fashion. Due to heavy reductions of both computational complexity and storage, our framework is a fully on-blockchain FL system that is feasible on simple smart contracts and therefore blockchain agnostic. We experimentally test our new framework and validate its theoretical properties.

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Trusted execution environment with Intel SGX

Cited by 9 publications

References 1 publication

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Survey of CPU Cache-Based Side-Channel Attacks: Systematic Analysis, Security Models, and Countermeasures

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

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