Collaborative Research: A Mathematical Framework for Generating Synthetic Data
协作研究:生成综合数据的数学框架
基本信息
- 批准号:2027299
- 负责人:
- 金额:$ 15万
- 依托单位:
- 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 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
由于安全审查障碍,许多与国家安全领域有关的数据集无法与学术界或工业界的专家共享,这大大阻碍了威胁检测研究的进展。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。这项研究准备通过开发一种严格的数学框架来解决这一重要问题,以确保合成数据的忠实和隐私保护。目标是创建一个尽可能真实的数据集,一个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的数据集。该项目的成果将在推进威胁检测和许多其他以隐私为关键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,以及他们在人工智能、信号处理、医疗诊断、威胁检测和通信工程领域将先进的数学概念转化为现实世界应用方面的专业知识。这项研究是尖端数学的几个领域与最先进的人工智能的融合。它寻求将优化、概率和机器学习等先进技术以稳健和高效的计算方法的形式引入数据科学。理论成果预计将以新的数学概念的形式出现,用于开发多模式可伸缩的合成数据。计算成果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,它还将成为对数学和数据科学前沿的研究和教育至关重要的那种跨学科活动的典范。为整个社会带来的回报很多,包括加强隐私保护,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、健康管理员、医疗信息系统开发人员、流行病学家、肿瘤学家和健康经济学家。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。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Privacy of Synthetic Data: A Statistical Framework
- DOI:10.1109/tit.2022.3216793
- 发表时间:2021-09
- 期刊:
- 影响因子:2.5
- 作者:M. Boedihardjo;T. Strohmer;R. Vershynin
- 通讯作者:M. Boedihardjo;T. Strohmer;R. Vershynin
Private measures, random walks, and synthetic data
- DOI:10.48550/arxiv.2204.09167
- 发表时间:2022-04
- 期刊:
- 影响因子:0
- 作者:M. Boedihardjo;T. Strohmer;R. Vershynin
- 通讯作者:M. Boedihardjo;T. Strohmer;R. Vershynin
Covariance’s Loss is Privacy’s Gain: Computationally Efficient, Private and Accurate Synthetic Data
- DOI:10.1007/s10208-022-09591-7
- 发表时间:2021-07
- 期刊:
- 影响因子:3
- 作者:M. Boedihardjo;T. Strohmer;R. Vershynin
- 通讯作者:M. Boedihardjo;T. Strohmer;R. Vershynin
Private sampling: a noiseless approach for generating differentially private synthetic data
- DOI:10.1137/21m1449944
- 发表时间:2021-09
- 期刊:
- 影响因子:0
- 作者:M. Boedihardjo;T. Strohmer;R. Vershynin
- 通讯作者:M. Boedihardjo;T. Strohmer;R. Vershynin
Algorithmically Effective Differentially Private Synthetic Data
- DOI:10.48550/arxiv.2302.05552
- 发表时间:2023-02
- 期刊:
- 影响因子:0
- 作者:Yi He;R. Vershynin;Yizhe Zhu
- 通讯作者:Yi He;R. Vershynin;Yizhe Zhu
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Roman Vershynin其他文献
Are most Boolean functions determined by low frequencies?
大多数布尔函数是由低频决定的吗?
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Roman Vershynin - 通讯作者:
Roman Vershynin
Hamiltonicity of Sparse Pseudorandom Graphs
稀疏伪随机图的哈密顿性
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Asaf Ferber;Jie Han;Dingjia Mao;Roman Vershynin - 通讯作者:
Roman Vershynin
The quarks of attention: Structure and capacity of neural attention building blocks
注意力的夸克:神经注意力构建模块的结构与容量
- DOI:
10.1016/j.artint.2023.103901 - 发表时间:
2023-06-01 - 期刊:
- 影响因子:4.600
- 作者:
Pierre Baldi;Roman Vershynin - 通讯作者:
Roman Vershynin
LECTURES ON FUNCTIONAL ANALYSIS
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Roman Vershynin - 通讯作者:
Roman Vershynin
Metric geometry of the privacy-utility tradeoff
隐私与效用权衡的度量几何
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
M. Boedihardjo;T. Strohmer;Roman Vershynin - 通讯作者:
Roman Vershynin
Roman Vershynin的其他文献
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{{ truncateString('Roman Vershynin', 18)}}的其他基金
High-Dimensional Probability for High-Dimensional Data
高维数据的高维概率
- 批准号:
1954233 - 财政年份:2020
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
Geometric functional analysis, random matrices and applications
几何泛函分析、随机矩阵及其应用
- 批准号:
1265782 - 财政年份:2013
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
Non-asymptotic problems on random operators in geometric functional analysis and applications
几何泛函分析中随机算子的非渐近问题及其应用
- 批准号:
1001829 - 财政年份:2010
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
FRG: Collaborative Research: Fourier analytic and probabilistic methods in geometric functional analysis and convexity
FRG:协作研究:几何泛函分析和凸性中的傅里叶分析和概率方法
- 批准号:
0918623 - 财政年份:2008
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
FRG: Collaborative Research: Fourier analytic and probabilistic methods in geometric functional analysis and convexity
FRG:协作研究:几何泛函分析和凸性中的傅里叶分析和概率方法
- 批准号:
0652617 - 财政年份:2007
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
Combinatorial and Probabilistic Approach to Geometric Functional Analysis and Applications
几何泛函分析和应用的组合和概率方法
- 批准号:
0401032 - 财政年份:2004
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
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