AitF: Provenance with Privacy and Reliability in Federated Distributed Systems

AitF：联邦分布式系统中隐私性和可靠性的起源

• 批准号: 1733794
• 负责人: Sampath Kannan
• 金额: $ 30.99万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733794&HistoricalAwards=false
• 关键词: AitF; Provenance; Privacy; Reliability; Federated

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.

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。

项目成果

Near-Perfect Recovery in the One-Dimensional Latent Space Model

一维潜在空间模型中近乎完美的恢复

• DOI: 10.1145/3366423.3380261
• 发表时间: 2020
• 期刊: Proceedings of the Web Conference 2020 (WWW '20
• 作者: Chen, Yu;Kannan, Sampath;Khanna, Sanjeev
• 通讯作者: Khanna, Sanjeev

Sublinear Algorithms and Lower Bounds for Metric TSP Cost Estimation

• DOI: 10.4230/lipics.icalp.2020.30
• 发表时间: 2020-06
• 作者: Yu Chen;Sampath Kannan;S. Khanna

Synchronization Schemas

• DOI: 10.1145/3452021.3458317
• 发表时间: 2021-06
• 期刊: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
• 作者: R. Alur;Phillip Hilliard;Z. Ives;Konstantinos Kallas;Konstantinos Mamouras;Filip Niksic;C. Stanford;V. Tannen;Anton Xue

Private resource allocators and their applications

• DOI: 10.1109/sp40000.2020.00065
• 发表时间: 2020-05
• 期刊: 2020 IEEE Symposium on Security and Privacy (SP)
• 作者: Sebastian Angel;Sampath Kannan;Zachary B. Ratliff

Linear Sketching over F_2

• DOI: 10.4230/lipics.ccc.2018.8
• 发表时间: 2018-06
• 作者: Sampath Kannan;Elchanan Mossel;Swagato Sanyal;G. Yaroslavtsev