Data aggregation has been widely implemented as an infrastructure of data-driven systems. However, a centralized data aggregation model requires a set of strong trust assumptions to ensure security and privacy. In recent years, decentralized data aggregation has become realizable based on distributed ledger technology. Nevertheless, the lack of appropriate centralized mechanisms like identity management mechanisms carries risks such as impersonation and unauthorized access. In this paper, we propose a novel decentralized data aggregation framework by leveraging self-sovereign identity, an emerging identity model, to lift the trust assumptions in centralized models and eliminate identity-related risks. Our framework formulates the aggregation protocol regarding data persistence and acquisition aspects, considering security, efficiency, flexibility, and compatibility. Furthermore, we demonstrate the applicability of our framework via a use case study where we concretize and apply our framework in a decentralized neuroscience data aggregation scenario.

翻译：数据聚合作为数据驱动系统的基础设施已被广泛实施。然而，集中式数据聚合模型需要一组强信任假设来确保安全性和隐私性。近年来，基于分布式账本技术，去中心化数据聚合已变得可实现。然而，缺乏如身份管理机制等适当的集中式机制会带来冒名顶替和未授权访问等风险。在本文中，我们提出了一种新颖的去中心化数据聚合框架，通过利用自主主权身份（一种新兴的身份模型）来消除集中式模型中的信任假设并规避与身份相关的风险。我们的框架从数据持久性和获取方面制定了聚合协议，兼顾安全性、效率、灵活性和兼容性。此外，我们通过一个用例研究展示了框架的适用性，在该研究中我们将框架具体化并应用于去中心化神经科学数据聚合场景。