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（全称：IMR：RI-P：安全灵活的实验数据集访问和共享-规划）是美国国家科学基金会网络基础设施规划拨款，致力于开发新方法，以支持联合数据共享，同时管理隐私。这项工作对于为协议设计、网络安全和真实数据隐私方面的新研究提供信息至关重要。然而，隐私要求使得网络数据难以共享，原因有几个：（1）数据应该匿名，但为了保留研究价值，匿名可能需要定制;（2）数据可能需要研究人员和数据提供者之间的明确协议，协议可以增强匿名性并使期望明确;（3）数据提供者和发起人需要跟踪数据使用，以展示和了解数据效用，并支持数据到期。该项目将开展外联活动，以了解社区在这些领域的需求，并将原型化方法和软件，以管理数据集请求批准，分发和评估的生命周期。该项目计划为支持数据集分发的生命周期开发开源软件路线图。此生命周期包括元数据管理、数据集请求和批准的有效处理以及灵活的数据集实现。其愿景是开发支持这些活动的开源软件，以及一个数据集提供者联合社区，这些数据集提供者可以自行托管或使用共享平台，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感和透明度。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。该项目网站是https://ant.isi.edu/safed/。该平台和软件将随着其开发和成熟而通过完整的提案提供。该奖项反映了NSF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。