ATD: A Mathematical Framework for Generating Synthetic Data

ATD：生成综合数据的数学框架

• 批准号: 2027248
• 负责人: Thomas Strohmer
• 金额: $ 35万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027248&HistoricalAwards=false
• 关键词: ATD; Mathematical; Framework; Generating; Synthetic; ATD; Mathematical; Framework; Generating; Synthetic

Progress in threat detection research is greatly hindered by the fact that many data sets related to areas of national security cannot be shared with experts in academia or industry due to security clearance barriers. The limited access to meaningful data sets prevents many researchers from contributing their expertise in algorithm development and verification. This research effort is poised to solve this important problem by developing a rigorous mathematical framework for the faithful and privacy-preserving generation of synthetic data. The goal is to create an as-realistic-as-possible dataset, one that not only maintains the nuances of the original data, but does so without endangering important pieces of sensible information. The results of this project will play a key role in advancing research in threat detection and many other fields where privacy is key. Strong expectation for success of this project is based on solid theoretical achievements by the investigators in high-dimensional probability, signal processing, and mathematical data science, as well as their expertise in turning advanced mathematical concepts into real-world applications in the areas of artificial intelligence, signal processing, medical diagnostics, threat detection, and communications engineering. This research effort is a fusion of several areas of cutting edge mathematics with state-of-the-art artificial intelligence. It seeks to bring advanced techniques from optimization, probability, and machine learning to data science in form of robust and efficient computational methods. Theoretical deliverables are expected to be in the form of new mathematical concepts for the development of multimodal scalable synthetic data. Computational deliverables will be in the form of numerical algorithms for privacy-protecting artificial intelligence. Beyond the project's broad technological impact, it will serve as a model for the kind of cross-disciplinary activity critical for research and education at the frontier of mathematics and data science. The payoffs for society at large are many, including increased privacy protection while maintaining the benefits of data-driven discovery. The users of synthetic data will include researchers in the national security sector, computer scientists, privacy experts, health administrators, medical information system developers, epidemiologists, oncologists and health economists.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.

威胁探测研究的进展极大地阻碍了以下事实：由于安全清除障碍，许多与国家安全领域有关的数据集不能与学术界或行业专家共享。对有意义的数据集的有限访问阻止了许多研究人员在算法开发和验证方面贡献其专业知识。 这项研究工作有望通过为忠实和隐私的合成数据开发严格的数学框架来解决这一重要问题。 目的是创建一个非常逼真的数据集，该数据集不仅要维护原始数据的细微差别，而且在不危害重要信息的情况下这样做。 该项目的结果将在推进威胁检测研究以及隐私是关键的许多其他领域中发挥关键作用。对该项目成功的强烈期望是基于研究人员在高维概率，信号处理和数学数据科学方面的扎实理论成就，及其在人工智能，信号处理，医疗诊断，威胁检测和通信工程领域中将高级数学概念转变为现实世界中的专业知识。 这项研究工作是融合了最先进数学领域与最先进的人工智能的融合。它试图以强大而有效的计算方法的形式将优化，概率和机器学习的先进技术带到数据科学。预计理论可交付成果将以新的数学概念的形式用于开发多模式可伸缩合成数据。 计算可交付成果将以用于保护人工智能的数值算法的形式。除了该项目的广泛技术影响外，它将成为数学和数据科学领域研究和教育至关重要的跨学科活动的模型。整个社会的回报是许多人，包括增加隐私保护，同时保持数据驱动的发现的好处。合成数据的用户将包括国家安全部门的研究人员，计算机科学家，隐私专家，卫生管理员，医疗信息系统开发人员，流行病学家，肿瘤学家和卫生经济学家。这项奖项反映了NSF的法定任务，并被认为是通过该基金会的知识功能和广泛影响的评估来评估Criteria的评估，并被认为是值得的。