EAGER: Foundations for the Systematic Study of Synthetic Data

EAGER：综合数据系统研究的基础

• 批准号: 2333225
• 负责人: Jaideep Vaidya
• 金额: $ 25万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333225&HistoricalAwards=false
• 关键词: EAGER; Foundations; Systematic; Study; Synthetic

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.

由于大量数据的可用性，已经实现了各个领域的数据驱动模型的进步。但是，人们对隐私权的担忧一直在增长，从而导致需要保护单个数据的同时仍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，同时删除任何可识别的信息，从而提供了潜在的解决方案。尽管已经提出了许多生成合成数据的方法，但在该领域仍缺乏固体理论基础。该项目弥合了理论与实践之间的差距。该项目的新颖性在于制定合成数据系统研究的基本原理，以及确定技术词汇和相关概念的基本原理。该项目的更广泛的意义和重要性在于其能够允许机构阐明，执行，评估和验证其对合成数据生成方法所需的约束，从而大大增加数据共享的所需约束。凭借更高的隐私和增强的实用性手头，该框架将塑造一个保护隐私，共享知识的世界，并且基于人工智能的方法确实是为了改善人类。该项目概念化了综合数据的哲学考虑，建立了合成数据的属性，开发了“合成”含义的正式定义，并为包括策划数据集，指标和基础的合成数据开发了一个全面的评估框架。该研究改善了我们对隐私，公用事业和合成数据的科学理解。该项目还通过为本科和研究生研究和外展活动提供新的安全性或隐私项目来培养研究和教育的整合，并作为一种非常宝贵的教学工具，并出色地进入了隐私和安全研究领域。该奖项反映了NSF的法规使命，并通过评估基金会的知识分子的评价和广泛的范围来诚实，并通过评估诚实地进行了评估。