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：数据聚合已经广泛地用于数据驱动系统的基础设施。然而，集中式数据聚合模型需要一系列强大的信任假设来确保安全和隐私。近年来，分布式账本技术使分散式数据聚合成为可能。然而，缺乏适当的身份管理机制，例如身份管理机制，存在冒名顶替和未经授权访问等风险。在本文中，我们提出了一种新颖的分散式数据聚合框架，利用 emerging identity model 自主主权身份来消除集中式模型中的信任假设和消除与身份相关的风险。我们的框架考虑了安全性、效率性、灵活性和兼容性等数据持久性和获取方面的聚合协议。此外，我们通过一个使用案例研究展示了我们框架的适用性，在一个分散式神经科学数据聚合场景中具体化和应用了我们的框架。