ATD: Robustness, Privacy, and Fairness in Threat Detection

该项目侧重于威胁检测的一般背景下的三个重要主题：鲁棒性，隐私性和公平性。需要稳健的程序来处理高度损坏和噪声数据。该项目将考虑稳健的图像生成及其在存在异常值和噪声的情况下对合成数据扩充的重要应用。在收集旨在服务于公共利益的汇总数据时，需要私人程序来保持个人的机密性。差异隐私已经成为解决这些问题的主要理论框架。该项目将开发有效的差分隐私算法，用于与威胁检测相关的降维。需要有公平的程序，以避免对少数群体的社会偏见的影响，以及基于个人或群体的特点对他们的任何偏见或偏袒。这在许多专注于异常的威胁检测应用中至关重要，其中代表性不足的个人可能会被不公平地描述为异常并因此容易受到威胁。该项目将重点关注与威胁检测相关的公平降维算法。将在不同的地理空间，人类动力学和成像数据集上测试所产生的算法。该项目将在第一年和第三年支持一名研究生，第二年支持一名博士后。该项目旨在开发与上述三个主题相关的有效算法和数学基础。鲁棒性研究的主要重点将是当生成任务的训练集被噪声或异常值（例如，具有来自不同类别的图像或完全损坏的图像，因此不能识别它们的典型结构）。该项目将探索一个端到端的深度神经网络，在不知道损坏或异常数据点的标签的情况下，在损坏的情况下稳健地生成高保真图像。隐私研究的主要重点将是差分隐私降维的理论和算法。差分隐私将通过结合噪声机制来获得。预期的理论将突出非凸性，光滑性和鲁棒性之间的相互作用，差分隐私。公平性研究的主要重点将是公平降维的理论和算法，其中公平性将通过最小化一个特殊的非凸能量函数来获得。该理论将强调非凸性和公平性之间的相互作用。该奖项反映了NSF的法定使命，并且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。

The project focuses on three important topics within the general context of threat detection: robustness, privacy and fairness. Robust procedures are needed for dealing with highly corrupted and noisy data. The project will consider robust generation of images and its important application to synthetic data augmentation in the presence of outliers and noise. Private procedures are needed to maintain confidentiality of individuals when collecting aggregate data that aims to serve the public interest. Differential privacy has emerged as the predominant theoretical framework for addressing such issues. The project will develop effective differentially-private algorithms for dimension reduction that are relevant to threat detection. Fair procedures are needed to avoid influences of social biases against minorities and any prejudice or favoritism towards an individual or a group based on their characteristics. This is crucial in many threat detection applications that focus on anomalies, where under-represented individuals may be unfairly profiled as anomalous and consequently threat-prone. The project will focus on fair dimension reduction algorithms that are relevant to threat detection. The resulting algorithms will be tested on different geospatial, human-dynamics and imaging datasets.This project will support one graduate student in the first and third years and one postdoc in the second year. The project aims to develop effective algorithms and mathematical foundations relevant to the above three stated themes. The main focus of the study of robustness will be on the generation of realistic images when the training set for the generative task is corrupted, either by noise or by outliers (e.g., having images from a different class or completely corrupted images so their typical structure cannot be recognized). The project will explore an end-to-end deep neural network to robustly generate high-fidelity images under corruption without knowing the labels of the corrupted, or anomalous, data points. The main focus of the study of privacy will be on theory and algorithms for differentially private dimension reduction. Differential privacy will be obtained by incorporating a noise mechanism. The expected theory will highlight the interaction between nonconvexity, smoothness, and robustness in relation to differential privacy. The main focus of the study of fairness will be on theory and algorithms for fair dimension reduction, where fairness will be obtained by minimizing a special non-convex energy function. The theory will highlight the interaction between nonconvexity and fairness.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.