Collaborative Research: A Mathematical Framework for Generating Synthetic Data

威胁检测研究的进展受到以下事实的极大阻碍：由于安全许可障碍，许多与国家安全领域相关的数据集无法与学术界或工业界的专家共享。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他们的专业知识。 这项研究工作准备通过开发一个严格的数学框架来解决这一重要问题，以忠实和保护隐私的方式生成合成数据。 我们的目标是创建一个尽可能真实的数据集，不仅保持原始数据的细微差别，而且不会危及重要的敏感信息。 该项目的成果将在推进威胁检测和许多其他隐私至关重要的领域的研究方面发挥关键作用。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坚实理论成就，以及他们在将先进数学概念转化为人工智能、信号处理、医疗诊断、威胁检测和通信工程领域的实际应用方面的专业知识。 这项研究工作是尖端数学的几个领域与最先进的人工智能的融合。它旨在将优化，概率和机器学习等先进技术以强大而有效的计算方法的形式引入数据科学。预计理论成果将以新的数学概念的形式出现，用于开发多模式可扩展的合成数据。 计算交付物将以保护隐私的人工智能的数值算法的形式出现。除了该项目广泛的技术影响外，它还将成为数学和数据科学前沿研究和教育的关键跨学科活动的典范。对整个社会的回报是多方面的，包括增加隐私保护，同时保持数据驱动发现的好处。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、卫生管理人员、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。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。

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.