AitF: Provenance with Privacy and Reliability in Federated Distributed Systems

这个项目的目标是调查联邦分布式系统中的起源问题，例如网络和科学工作流，在这些系统中，几个自利的实体生成和共享数据，并计算和做出关于共享数据的决策。计算和决策本身就产生了新的共享数据。来源分析使我们能够了解各种实体对数据的贡献。这个项目试图通过将半分类起源的理论概念与网络起源的实际方法统一起来，将起源建立在良好的数学基础上。私人投资公司还希望设计新的算法和压缩技术来收集和管理种源数据。该项目的第二个重点是计算每个数据项的可靠性分数，并将其与它们的出处相关联。为了做到这一点，PI将设计为原始数据项分配可靠性分数的技术，以及基于合理的公理原则为派生数据项分配可靠性分数的演算。该项目将产生广泛的影响，使网络能够更可靠地运作，并提高科学工作流程的可重复性。

The goal of this project is to investigate the question of provenance in federated distributed systems, such as networks and scientific workflows, where several self-interested entities generate and share data, and compute and make decisions on the shared data. Computations and decisions give rise themselves to new shared data. Provenance analysis allows us to understand the contributions of the various entities to the data. This project seeks to put provenance on a sound mathematical foundation by unifying the theoretical notion of semiring provenance with practical approaches to network provenance. The PIs would also like to design new algorithms and compression techniques for collecting and managing provenance data. A second thrust of the project is to compute and associate reliability scores to each data item, in conjunction with their provenance. In order to do this, the PIs will design techniques for assigning reliability scores to primitive data items, as well as a calculus based on sound axiomatic principles for assigning reliability scores to derived data items. This project will achieve broad impact by allowing for networks to operate more reliably and by enhancing reproducibility in scientific workflows.