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

SAFED-ASP项目（全称：IMR: RI-P：安全和灵活的实验数据集访问和共享—规划）是NSF网络基础设施规划拨款，致力于在管理隐私的同时支持联邦数据共享的新方法。这项工作对于协议设计、网络安全和真实世界数据隐私的新研究至关重要。然而，隐私要求使得网络数据难以共享，原因如下：(1)数据应该匿名化，但为了保留研究价值，匿名化可能需要定制；(2)数据可能需要研究人员和数据提供者之间的明确协议，协议可以增强匿名化并使期望明确；(3)数据提供者和赞助者需要跟踪数据的使用，以证明和理解数据的效用，并支持数据过期。该项目将开展外展活动，以了解这些领域的社区需求，并将对方法和软件进行原型化，以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制定一个路线图，以支持数据集分发的生命周期。这个生命周期包括元数据管理、有效处理数据集请求和批准，以及灵活的数据集实现。其愿景是开发开源软件来支持这些活动，以及一个由数据集提供者组成的联合社区，这些数据集提供者要么自行托管，要么使用共享平台。这项工作的更广泛影响是为新方法铺平道路，这些新方法将减少数据共享中的摩擦，并提高数据处理的责任和透明度。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。项目网站为https://ant.isi.edu/safed/。该平台和软件将随着其开发和通过完整提案的成熟而变得可用。开发的工具预计将在此站点托管，也适合自托管。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.