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IMR: RI-P: Safe And Flexible Experimental Dataset Access and Sharing-Planning (SAFED-ASP)

IMR: RI-P: Safe And Flexible Experimental Dataset Access and Sharing-Planning (SAFED-ASP)
IMR:RI-P:安全灵活的实验数据集访问和共享规划 (SAFED-ASP)
批准号:
2224467
负责人:
John Heidemann
金额:
$9.91万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-10-01 至 2023-07-31

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中文摘要
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英文摘要
The SAFED-ASP Project (full title: IMR: RI-P: Safe And Flexible Experimental Dataset Access and Sharing--Planning) is an NSF cyberinfrastructure planning grant to work towards new methods to support federated data sharing while managing privacy. This work will be critical to inform new research in protocol design, cybersecurity, and privacy with real-world data. However, privacy requirements make network data difficult to share for several reasons: (1) Data should be anonymized, but to retain research value, that anonymization may require customization; (2) Data may require explicit agreements between researchers and data providers, agreements can augment anonymization and make expectations explicit; (3) Data providers and sponsors need to track data use to demonstrate and understand data utility and support data expiration. This project will conduct outreach to understand community requirements in these areas, and will prototype methods and software to manage the lifecycle of dataset request approval, distribution, and evaluation.This project plans to develop a roadmap for open-source software that will support the lifecycle of dataset distribution. This lifecycle includes metadata management, efficient handling of datasets request and approvals, and flexible dataset fulfillment. The vision is to develop open-source software to support these activities, and a federated community of dataset providers that either self-host or use a shared platform.The broader impact of this work is to pave the way to new approaches that will will reduce friction in data sharing and improve data handling responsibility and transparency. Broader dataset availability is key to applying machine learning and big-data techniques to networking and security problems.The project website is https://ant.isi.edu/safed/. The platform and software will become available as it is developed and as it matures through a full proposal. Tools developed is expected to be hosted at this site and is also suitable for self-hosting.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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