AitF: Provenance with Privacy and Reliability in Federated Distributed Systems

该项目的目标是研究联邦分布式系统中的起源问题，例如网络和科学工作流，其中几个自私的实体生成和共享数据，并对共享数据进行计算和决策。 计算和决策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。 这个项目试图把起源一个健全的数学基础上统一的理论概念的半环起源与实际的方法网络起源。 PI还希望设计新的算法和压缩技术来收集和管理来源数据。 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将设计技术，为原始数据项分配可靠性分数，以及基于合理的公理原则的演算，为派生数据项分配可靠性分数。 该项目将通过允许网络更可靠地运行和提高科学工作流程的可重复性来实现广泛的影响。

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.