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: University of Southern California
• 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
• 起止时间: 2022-10-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224467&HistoricalAwards=false
• 关键词: IMR; RI; Safe; Flexible; Experimental

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.

SAFED-ASP项目（完整标题：IMR：RI-P：安全且灵活的实验数据集访问和共享 - 计划）是NSF网络基础设施计划拨款，旨在努力采用新方法，以支持联合数据共享，同时管理隐私。这项工作对于使用现实世界数据为协议设计，网络安全和隐私方面的新研究至关重要。但是，隐私要求使网络数据很难共享，原因有几个原因：（1）数据应被匿名化，但要保留研究值，匿名化可能需要自定义； （2）数据可能需要研究人员与数据提供商之间的明确协议，协议可以增强匿名化并明确提出期望； （3）数据提供商和赞助商需要跟踪数据用途，以演示和理解数据实用程序并支持数据到期。该项目将进行宣传以了解这些领域的社区要求，并将原型方法和软件管理数据集请求批准，分发和评估的生命周期。该项目计划为开源软件开发路线图，以支持数据集分布的生命周期。该生命周期包括元数据管理，有效地处理数据集请求和批准以及灵活的数据集实现。愿景是开发开源软件来支持这些活动，以及一个自助宿​​主或使用共享平台的数据集提供商的联合社区。这项工作的更广泛的影响是为新方法铺平道路，以减少数据共享中的摩擦并提高数据处理责任和透明度。更广泛的数据集可用性是将机器学习和大数据技术应用于网络和安全问题的关键。项目网站是https://ant.isi.edu/safed/。该平台和软件将随着其开发，并通过完整的提案成熟。预计开发的工具将在此网站上托管，也适合自我托管。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。