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

合作研究：III：媒介：计算隐私和机器学习的综合框架

• 批准号: 2212175
• 负责人: Fei Wang
• 金额: $ 26.51万
• 依托单位: Joan and Sanford I. Weill Medical College of Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212175&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; consolidated

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.

在过去的几年中，机器学习已增强，从图像和语音处理到疾病诊断中的各种领域中的应用。尽管机器学习技术取得了巨大的成功，但仍收集了大量数据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。现有的努力仍然是初步的，巨大的挑战仍有待解决。至关重要的是，更强大的隐私保护通常会牺牲机器学习模型的重要特性，例如预测效用和公平性，这是不受欢迎的或完全不可接受的。该项目为机器学习系统开发了一个合并的隐私保护框架，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，公平和分布式学习。该项目将提供一组全面的工具，以保护在不同情况下为现实世界的机器学习应用程序保护数据隐私。保护隐私技术将对各个部门使用的机器学习系统产生变革性的影响，使公司和医院能够在大数据上享受机器学习技术的优势，同时在相应的法规下保护数据隐私。该研究项目在相应的研究项目中彻底审查并讨论了现实性的复杂性或限制，从而在差异上分配了差异性，并限制了差异性的私有性，而差异性地分配，涉及隐私性，限制，并将其分配给隐私权，并将其分配到隐私性，限制，并将其分配到隐私性，限制，并将其分配到隐私性，限制，并将其分配到隐私性，并将学习后隐私保护。该项目开发的框架深入扎根于严格的优化框架，通常伴随着理论保证，并得到了尖端的算法工具，例如元学习，对抗性学习和联合学习。此外，该框架还带有以下方法论创新：针对学习问题量身定制的差异隐私；定制的隐私在协作学习中解决异质性；通过学习对学习模型的隐私保护；巩固学习的隐私和公平性。这些努力将大大增强差异隐私的实用性和可扩展性。该项目将在各种现实世界中进行系统评估，并且该工具将很容易用于应对医学研究中的关键挑战。结果将在本科和研究生级别的多个课程中纳入多个课程。研究成果将通过开源软件的发布和讲习班，本科研究的参与以及向K-12教育的推广，重点关注STEM教育中的少数群体和代表性不足的团体。不同层次和学科，STEM和文科的学生将参加有关隐私和机器学习的研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查标准通过评估来获得支持的。

项目成果

Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.

• DOI: 10.1038/s41598-023-27856-1
• 发表时间: 2023-01-12
• 期刊: Scientific reports
• 影响因子: 4.6

Patient Similarity Learning with Selective Forgetting

通过选择性遗忘进行患者相似性学习

• DOI: 10.1109/bibm55620.2022.9995016
• 发表时间: 2022
• 期刊: IEEE International Conference on Bioinformatics and Biomedicine (BIBM
• 作者: Qian, Wei;Zhao, Chenxu;Shao, Huajie;Chen, Minghan;Wang, Fei;Huai, Mengdi
• 通讯作者: Huai, Mengdi