Acurast: Decentralized Serverless Cloud

• URL: http://arxiv.org/abs/2503.15654v1
• Date: Wed, 19 Mar 2025 19:09:21 GMT
• Title: Acurast: Decentralized Serverless Cloud
• Authors: Christian Killer, Alessandro De Carli, Pascal Brun, Amadeo Victor Charlé, Mike Godenzi, Simon Wehrli,
• Abstract summary: Acurast is a serverless cloud with a purpose-built orchestrator and reputation engine.<n>Developers can off-load their computations and verify cryptographically.<n>Acurast offers a modular execution layer, taking advantage of secure hardware and trusted execution environments.
• Score: 37.860555651161796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Centralized trust is ubiquitous in today's interconnected world, from computational resources to data storage and its underlying infrastructure. The monopolization of cloud computing resembles a feudalistic system, causing a loss of privacy and data ownership. Cloud Computing and the Internet in general face widely recognized challenges, such as (1) the centralization of trust in auxiliary systems (e.g., centralized cloud providers), (2) the seamless and permissionless interoperability of fragmented ecosystems and (2) the effectiveness, verifiability, and confidentiality of the computation. Acurast is a decentralized serverless cloud that addresses all these shortcomings, following the call for a global-scale cloud founded on the principles of the open-source movement. In Acurast, a purpose-built orchestrator, a reputation engine, and an attestation service are enshrined in the consensus layer. Developers can off-load their computations and verify executions cryptographically. Furthermore, Acurast offers a modular execution layer, taking advantage of secure hardware and trusted execution environments, removing the trust required in third parties, and reducing them to cryptographic hardness assumptions. With this modular architecture, Acurast serves as a decentralized and serverless cloud, allowing confidential and verifiable compute backed by the hardware of security and performance mobile devices.

Related papers

• Trusted Compute Units: A Framework for Chained Verifiable Computations [41.94295877935867]
  This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable computations across heterogeneous technologies. By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability.
  arXiv  Detail & Related papers  (2025-04-22T09:01:55Z)
• SoK: A cloudy view on trust relationships of CVMs -- How Confidential Virtual Machines are falling short in Public Cloud [1.7040315536962174]
  Confidential computing in the public cloud aims to safeguard workload privacy while outsourcing infrastructure management to a cloud provider.<n>This SoK critically examines the confidential computing offerings of market-leading cloud providers to assess whether they genuinely adhere to its core principles.
  arXiv  Detail & Related papers  (2025-03-11T10:21:29Z)
• Authentication and identity management based on zero trust security model in micro-cloud environment [0.0]
  The Zero Trust framework can better track and block external attackers while limiting security breaches resulting from insider attacks in the cloud paradigm. This paper focuses on authentication mechanisms, calculation of trust score, and generation of policies in order to establish required access control to resources.
  arXiv  Detail & Related papers  (2024-10-29T09:06:13Z)
• Privacy-Preserving Decentralized AI with Confidential Computing [0.7893328752331561]
  This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network. CC leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data. We explore how we can integrate TEEs into Atoma's decentralized framework.
  arXiv  Detail & Related papers  (2024-10-17T16:50:48Z)
• Advocate -- Trustworthy Evidence in Cloud Systems [39.58317527488534]
  The rapid evolution of cloud-native applications, characterized by dynamic, interconnected services, presents significant challenges for maintaining trustworthy and auditable systems. Traditional methods of verification and certification are often inadequate due to the fast-past and dynamic development practices common in cloud computing. This paper introduces Advocate, a novel agent-based system designed to generate verifiable evidence of cloud-native application operations.
  arXiv  Detail & Related papers  (2024-10-17T12:09:26Z)
• Decentralized Credential Status Management: A Paradigm Shift in Digital Trust [0.0]
  Public key infrastructures are essential for Internet security, ensuring robust certificate management and revocation mechanisms. The transition from centralized to decentralized systems presents challenges such as trust distribution and privacy-preserving credential management. This paper explores the evolution of certificate status management from centralized to decentralized frameworks, focusing on blockchain technology and advanced cryptography.
  arXiv  Detail & Related papers  (2024-06-17T13:17:56Z)
• Mitigating Data Sharing in Public Cloud using Blockchain [0.0]
  We propose a secure data ecosystem in the cloud with the key aspects being Data Rights, Data Sharing, and Data Validation. This will ensure that existing public cloud-based systems can easily deploy blockchain enhancing trustworthiness and non-repudiation of cloud data.
  arXiv  Detail & Related papers  (2024-04-21T13:12:44Z)
• Performance Analysis of Decentralized Physical Infrastructure Networks and Centralized Clouds [42.37170902465878]
  Decentralized Physical Infrastructure Networks (DePINs) aim to enhance data sovereignty and confidentiality and increase resilience against a single point of failure. This work focuses on the potential of DePINs to disrupt traditional centralized architectures by taking advantage of the Internet of Things (IoT) devices and crypto-economic design in combination with blockchains.
  arXiv  Detail & Related papers  (2024-04-12T08:00:38Z)
• HasTEE+ : Confidential Cloud Computing and Analytics with Haskell [50.994023665559496]
  Confidential computing enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs) TEEs offer low-level C/C++-based toolchains that are susceptible to inherent memory safety vulnerabilities and lack language constructs to monitor explicit and implicit information-flow leaks. We address the above with HasTEE+, a domain-specific language (cla) embedded in Haskell that enables programming TEEs in a high-level language with strong type-safety.
  arXiv  Detail & Related papers  (2024-01-17T00:56:23Z)
• Collaborative Unsupervised Visual Representation Learning from Decentralized Data [34.06624704343615]
  We propose a novel federated unsupervised learning framework, FedU. In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network. FedU preserves data privacy as each party only has access to its raw data.
  arXiv  Detail & Related papers  (2021-08-14T08:34:11Z)
• Regulation conform DLT-operable payment adapter based on trustless - justified trust combined generalized state channels [77.34726150561087]
  Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks. We give an overview of current solutions that differ in their fundamental values and technological possibilities. We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
  arXiv  Detail & Related papers  (2020-07-03T10:45:55Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.