EAGER: Foundations for the Systematic Study of Synthetic Data

由于大量数据的可用性，自然语言处理、计算机视觉和机器人等各个领域的数据驱动模型的进步已经成为可能。然而，对隐私权的担忧一直在增长，导致需要在保护个人数据的同时仍然允许数据共享的策略。合成数据通过保留原始数据的统计特性，同时删除任何个人身份信息，提供了一种潜在的解决方案。虽然已经提出了许多生成合成数据的方法，但在这一领域仍然缺乏坚实的理论基础。这个项目弥合了理论和实践之间的差距。该项目的创新之处在于制定了系统研究合成数据的基本原则，并澄清了技术词汇和相关概念。该项目更广泛的意义和重要性在于它能够让机构阐明、执行、评估和验证其合成数据生成方法所需的限制，从而大大加快数据共享。通过提高隐私和增强实用性，这个框架将塑造一个隐私得到保护，知识得到共享，基于人工智能的方法真正蓬勃发展的世界，以造福人类。该项目将综合数据的哲学考虑概念化，建立综合数据的属性，制定“综合”的正式定义，并为综合数据开发一个全面的评估框架，包括策划的数据集，指标和基线。这项研究提高了我们对隐私，实用性和合成数据的科学理解。该项目还通过为本科生和研究生的研究和推广活动提供新的安全或隐私项目，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

Advancements in data-driven models in various fields like natural language processing, computer vision, and robotics have been made possible due to the availability of large amounts of data. However, concerns about privacy rights have been growing, leading to the need for strategies that protect individual data while still allowing data sharing. Synthetic data offers a potential solution by preserving the statistical properties of the original data while removing any personally identifiable information. Although many methods for generating synthetic data have been proposed, there is still a lack of solid theoretical foundations in this area. This project bridges the gap between theory and practice. The project's novelties are in developing the fundamental principles for the systematic study of synthetic data, and in clarifying the technical vocabulary and the associated concepts. The project's broader significance and importance is in its ability to allow institutions to articulate, enforce, evaluate, and validate their required constraints for synthetic data generation methodologies, significantly accelerating data-sharing. With heightened privacy and enhanced utility hand in hand, this framework will shape a world where privacy is safeguarded, knowledge is shared, and AI-based methods truly flourish for the betterment of humanity. The project conceptualizes the philosophical considerations of synthetic data, establishes properties of synthetic data, develops formal definitions of what it means to be “synthetic”, and develops a comprehensive evaluation framework for synthetic data that includes curated datasets, metrics, and baselines. The research improves our scientific understanding of privacy, utility, and synthetic data. The project also cultivates the integration of research and education, by providing new security or privacy projects for undergraduate and graduate research and outreach activities, and serves as an invaluable teaching tool and excellent entry point into the field of privacy and security research.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.