EAGER: Foundations for the Systematic Study of Synthetic Data

由于大量数据的可获得性，数据驱动模型在自然语言处理、计算机视觉和机器人等各个领域的进步成为可能。然而，对隐私权的担忧一直在增长，这导致需要制定策略，在保护个人数据的同时仍允许数据共享。合成数据提供了一种潜在的解决方案，它保留了原始数据的统计属性，同时删除了任何个人身份信息。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。这个项目在理论和实践之间架起了一座桥梁。该项目的创新之处在于制定了对合成数据进行系统研究的基本原则，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，它能够允许各机构阐明、执行、评估和验证它们对合成数据生成方法所需的限制，从而显著加快数据共享。随着隐私的提高和效用的增强，这一框架将塑造一个隐私得到保护、知识共享、基于人工智能的方法真正蓬勃发展的世界，以改善人类。该项目概念化了合成数据的哲学考虑，确立了合成数据的属性，对“合成”的含义进行了正式定义，并开发了一个合成数据的综合评估框架，其中包括精选的数据集、度量和基线。这项研究提高了我们对隐私、效用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，培养研究和教育的一体化，并作为一个宝贵的教学工具和进入隐私和安全研究领域的极好切入点。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.