Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things. Unfortunately, when trusted third parties are removed to achieve this decentralization, ensuring an outsourced computation is performed correctly remains a significant challenge. In this paper, we provide a solution to this problem. We outline Marvel DC, a fully decentralized blockchain-based distributed-computing protocol which formally guarantees that computers are strictly incentivized to correctly perform requested computations. Furthermore, Marvel DC utilizes a reputation management protocol to ensure that, for any minority of computers not performing calculations correctly, these computers are identified and selected for computations with diminishing probability. We then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which decouples results from the computers which computed them, making the protocol suitable for computations such as Federated Learning, where results can reveal sensitive information about that computer that computed them. We provide an implementation of Marvel DC and analyses of both protocols, demonstrating that they are not only the first protocols to provide the aforementioned formal guarantees, but are also practical, competitive with prior attempts in the field, and ready to deploy.
翻译:分散计算外包应允许任何人获得在物联网中存在的大量计算能力。 不幸的是,如果消除了受信任的第三方以实现这种分散化,那么确保正确进行外包计算仍是一个重大挑战。 在本文件中,我们提供了解决这一问题的解决方案。我们概述了完全分散的基于链条的分布式计算协议Marvel DC, 正式保证计算机得到严格激励以正确进行所要求的计算。此外, Marvel DC 使用一个名声管理协议,以确保对于没有正确进行计算的任何少数计算机来说,这些计算机被识别和选择用于概率越来越低的计算。我们随后概述了隐私 Marvel DC, 隐私增强版的Marvel DC, 由计算计算机拆分, 使协议适合于计算, 如Feded Learning等计算机的敏感信息。我们提供了 Marvel DC 和对这两个协议的分析, 表明它们不仅是第一个提供上述正式保证的协议, 而且是实际的, 具有竞争力的, 并准备在实地进行部署。