SATC: CORE: Medium: Principles and Algorithms for Visual Data Exploration Under Differential Privacy

数据科学的一个基本部分是可视化数据探索，它使用可视化和用户交互来促进从数据中发现新知识和可操作的见解。可视化数据探索得到了当前可视化分析技术的良好支持。然而，在许多领域，正在探索的数据包括关于个人的个人事实。因此，对数据的访问可能受到隐私政策或法规的限制。该项目将开发新的可视化数据探索技术，在支持发现新知识的同时，确保个人隐私得到保护。这些技术将在医疗保健数据、政府管理数据和移动性数据的背景下进行研究。该项目将扩大对私人数据的安全和有效探索，使更广泛的数据科学家社区能够从更丰富的数据源产生见解，包括那些以前因隐私问题而被禁止的数据源。该项目中开发的可视化探索方法将为差异隐私模型提供保证，该模型正在成为保护个人数据的主导标准。在提供不同隐私保证的同时，要实现对数据的准确视觉探索，需要隐私算法、可视化技术以及仔细的评估方法和人类受试者实验方面的新进展。在不同隐私下支持数据可视化的根本挑战来自隐私算法和可视化技术之间的复杂交互。如果针对可视化任务定制私有数据发布算法，则可以更好地设计这些算法。需要使用特殊的可视化方法来处理有噪声的私有数据，包括那些传递不确定性和对虚假视觉伪像具有健壮性的数据。拟议的研究有可能通过以下方式改变私人数据的使用：(I)研究如何在存在由不同的私人算法引入的噪声的情况下调整当前的可视化和交互技术；(Ii)开发新的算法，以提高静态可视化和交互式视觉探索的视觉准确性；以及(Iii)提供一个基准和评估标准，以加速私人可视化的创新。这些算法的有效性和价值将通过运行一系列以人为中心的评估进行经验性评估。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。

A fundamental part of data science is visual data exploration, which uses visualization and user interaction to facilitate the discovery of new knowledge and actionable insights from data. Visual data exploration is well-supported by current visual analytics technology. However, in many domains, the data being explored includes personal facts about individuals. Access to the data may therefore be limited by privacy policies or regulations. This project will develop novel visual data exploration technology that can support the discovery of new knowledge while at the same time guaranteeing that individuals’ privacy is protected. These technologies will be studied in the context of healthcare data, government administrative data, and mobility data. This project will expand the safe and effective exploration of private data, allowing a broader community of data scientists to generate insights from a richer set of data sources, including those previously off-limits due to privacy concerns. The visual exploration methods developed in this project will provide guarantees in the model of differential privacy, which is emerging as the dominant standard for protecting personal data. Enabling accurate visual exploration of data while offering a guarantee of differential privacy requires novel advances in privacy algorithms, visualization technology, as well as careful evaluation methodology and experiments with human subjects. The fundamental challenges of supporting data visualization under differential privacy stem from the complex interaction between privacy algorithms and visualization techniques. Algorithms for private data release can be better designed if they are customized to visualization tasks. And special visualization methods need to be used with noisy privatized data, including those that communicate uncertainty and are robust to spurious visual artifacts. The proposed research has the potential to transform the use of private data by (i) investigating how current visualization and interaction techniques should be adapted in the presence of noise introduced by differentially private algorithms, (ii) developing new algorithms that offer better visual accuracy, for both static visualizations and interactive visual exploration, and (iii) providing a benchmark and evaluation standards to accelerate innovation in private visualization. The effectiveness and value of these algorithms will be evaluated empirically by running a series of human-centered evaluations.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.