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。该项目将开发新的视觉数据探索技术，以支持新知识的发现，同时确保个人隐私得到保护。这些技术将在医疗数据、政府管理数据和移动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中产生见解，包括那些以前因隐私问题而被禁止的数据源。该项目中开发的视觉探索方法将在差异隐私模型中提供保证，这正在成为保护个人数据的主要标准。在提供差异隐私保证的同时实现对数据的准确视觉探索，需要在隐私算法、可视化技术以及仔细的评估方法和人类受试者实验方面取得新的进展。在差异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。私有数据发布的算法可以更好地设计，如果他们是定制的可视化任务。特殊的可视化方法需要用于有噪音的私有化数据，包括那些传达不确定性并且对虚假视觉伪影具有鲁棒性的方法。拟议的研究有可能通过以下方式改变私有数据的使用：（i）调查当前的可视化和交互技术如何适应差异私有算法引入的噪声，（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本的评估进行经验评估。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。

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.