Collaborative Research: III: Medium: A consolidated framework of computational privacy and machine learning

在过去的几年里，机器学习已经变得越来越突出，在从图像和语音处理到疾病诊断的各个领域都有应用。尽管机器学习技术取得了巨大的成功，但大量的数据被收集并用于训练机器学习模型。敏感数据的隐私已成为一个大问题。现有的努力仍处于初步阶段，还有巨大的挑战有待解决。至关重要的是，更强的隐私保护保证往往会牺牲机器学习模型的重要属性，比如预测效用和公平性，这可能是不希望的或完全不可接受的。该项目为机器学习系统开发了一个统一的隐私保护框架，该框架全面考虑了计算隐私与机器学习的几个关键属性之间的最佳权衡，包括效用、公平性和分布式学习。该项目将提供一套全面的工具，以保护不同情况下真实世界机器学习应用程序的数据隐私。隐私保护技术将对各个部门使用的机器学习系统产生变革性影响，使公司和医院能够享受机器学习技术在大数据上的优势，同时在相应的法规下保护数据隐私。本研究项目从隐私-效用权衡、隐私-公平关系、分布式学习中的隐私、学习后隐私保护等方面深入探讨了差分隐私应用在现实世界中的复杂性或局限性。项目开发的框架深深扎根于严格的优化框架，通常伴随着理论保证，并辅以元学习、对抗学习和联邦学习等前沿算法工具。此外，该框架还进行了以下方法论创新：针对学习问题量身定制的差分隐私；个性化隐私解决协同学习中的异质性学习模型的遗忘隐私保护巩固学习的隐私和公平。这些努力将显著增强差分隐私的实用性和可扩展性。该项目将对各种现实世界的医学应用进行系统评估，并且这些工具将随时用于解决医学研究中的关键挑战。研究结果将被纳入本科和研究生阶段的多个课程。研究成果将通过开源软件发布和研讨会、本科生研究的参与，以及向K-12教育推广，广泛而全面地传播，重点关注STEM教育中的少数民族和代表性不足的群体。不同层次和学科的学生，STEM和文科，将参与关于隐私和机器学习的研究。该奖项反映了美国国家科学基金会的法定使命，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。

Machine learning has grown to increase prominence over the past years, finding applications in various domains from image and speech processing to disease diagnosis. Despite the great success of machine learning techniques, massive amounts of data are collected and used to train the machine learning models. The privacy of sensitive data has become a big concern. Existing efforts are still preliminary, and enormous challenges remain to be resolved. Crucially, stronger privacy protection guarantees often sacrifice important properties of machine learning models, such as predictive utility and fairness, which can be undesirable or completely unacceptable. This project develops a consolidated privacy protection framework for machine learning systems that comprehensively considers the optimal trade-offs between computational privacy and several critical properties of machine learning, including utility, fairness, and distributed learning. The project will provide a comprehensive set of tools to protect data privacy for real-world machine learning applications under different circumstances. The privacy-preserving techniques will have a transformative impact on machine learning systems used by various sectors, allowing companies and hospitals to enjoy the advantages of machine learning techniques on big data while protecting data privacy under corresponding regulations.The research project thoroughly examines and discusses the real-world complicacy or restrictions when applying differential privacy, from privacy-utility trade-off, privacy-fairness relation, privacy in distributed learning, to post-learning privacy protection. The framework developed by the project takes deep root in rigorous optimization frameworks, often accompanied by theoretical guarantees and aided by cutting-edge algorithmic tools such as meta-learning, adversarial learning, and federated learning. Besides, the framework carries the following methodological innovations: differential privacy tailored to learning problems; customized privacy addressing heterogeneity in collaborative learning; privacy-protection of learned models through unlearning; consolidated privacy and fairness in learning. Those efforts will significantly augment the practicality and scalability of differential privacy. The project will be systematically evaluated on various real-world medical applications, and the tools will be readily used to tackle critical challenges in medical research. The outcomes will be incorporated into multiple courses at both undergraduate and graduate levels. The research outcomes will be disseminated broadly and comprehensively through open-source software releases and workshops, the involvement of undergraduate research, and outreach to K-12 education, focusing on minorities and under-representative groups in STEM education. Students at different levels and disciplines, STEM and liberal arts, will be participating in the research on privacy and machine learning.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.