Leveraging Self-Sovereign Identity in Decentralized Data Aggregation
Leveraging Self-Sovereign Identity in Decentralized Data Aggregation
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DOI：
10.1109/sds57574.2022.10062918
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Yepeng Ding;Hiroyuki Sato;Maro G. Machizawa
中科院分区：
文献类型：
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作者：
Yepeng Ding;Hiroyuki Sato;Maro G. Machizawa
数据聚合已被广泛实施，作为数据驱动系统的基础架构。但是，集中的数据聚合模型需要一组强大的信任假设，以确保安全和隐私。近年来，根据分布式分类帐技术，分散的数据聚合已变得可实现。然而，缺乏适当的集中机制，例如身份管理机制，具有模仿和未经授权访问的风险。在本文中，我们提出了一个新型的分散数据聚合框架，它利用自我主张身份（一种新兴身份模型）来提高集中式模型中的信任假设并消除与身份相关的风险。我们的框架考虑了安全性，效率，灵活性和兼容性，制定了有关数据持久性和获取方面的汇总协议。此外，我们通过用例研究证明了我们的框架的适用性，在该用例研究中，我们将框架和应用框架应用于分散的神经科学数据聚合方案。
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.