分布式环境下契约式数据共享的概念体系结构
A Conceptual Architecture for Contractual Data Sharing in a Decentralised Environment
摘要：
机器学习系统依赖于数据进行训练、输入和持续的反馈和验证。外地的数据可能来自不同的来源，数据的最终用户往往是匿名的或不知道的。无论何时获取和使用数据，其消费者都需要确保数据的准确性符合描述，数据是合法获得的，并且他们需要了解数据提供的条款，以便他们能够遵守这些条款。同样，数据提供者也需要保证其数据被授权方根据其条款合法使用，并且使用得到适当的补偿。此外，双方可能希望就特定的一组服务质量（QoS）度量达成一致，该度量可用于基于成本协商服务质量，然后接收在那些商定的QoS级别内提供数据的确认。在这里，我们提出了一个概念架构，使数据共享协议进行编码和计算强制执行，报酬时，需要的，和一个值得信赖的审计跟踪，以供以后的分析或复制的环境。我们的架构使用基于区块链的分布式账本技术，可以在各方没有建立信任关系或集中式命令和控制结构的情况下促进交易。我们探索的技术，以促进信心所提供的数据的准确性，并让数据用户确定数据质量和成本之间的权衡。我们的系统举例说明，通过考虑一个案例研究，使用来自不同方面的多个数据源，以监测城市地区的交通水平。
英文摘要：
Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ultimate users of the data. Whenever data is sourced and used, its consumers need assurance that the data accuracy is as described, that the data has been obtained legitimately, and they need to understand the terms under which the data is made available so that they can honour them. Similarly, suppliers of data require assurances that their data is being used legitimately by authorised parties, in accordance with their terms, and that usage is appropriately recompensed. Furthermore, both parties may want to agree on a specific set of quality of service (QoS) metrics, which can be used to negotiate service quality based on cost, and then receive affirmation that data is being supplied within those agreed QoS levels. Here we present a conceptual architecture which enables data sharing agreements to be encoded and computationally enforced, remuneration to be made when required, and a trusted audit trail to be produced for later analysis or reproduction of the environment. Our architecture uses blockchain-based distributed ledger technology, which can facilitate transactions in situations where parties do not have an established trust relationship or centralised command and control structures. We explore techniques to promote faith in the accuracy of the supplied data, and to let data users determine trade-offs between data quality and cost. Our system is exemplified through consideration of a case study using multiple data sources from different parties to monitor traffic levels in urban locations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
