CAREER: Extending the Foundations of Privacy-Preserving Machine Learning

职业：扩展隐私保护机器学习的基础

• 批准号: 2144532
• 负责人: Raef Bassily
• 金额: $ 50.01万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144532&HistoricalAwards=false
• 关键词: CAREER; Extending; Foundations; Privacy; Preserving

Despite their numerous societal benefits, modern machine learning algorithms pose real threats to personal privacy. Differential privacy is a mathematical framework that enables designing learning algorithms with provable privacy guarantees for their input datasets. Despite the recent progress in differentially private (DP) machine learning, our current understanding of the fundamental characteristics of DP learning algorithms is very limited. This career project offers a multifaceted research plan that tackles a broad range of fundamental questions in two important areas of DP machine learning: (i) stochastic optimization and (ii) federated learning. The first is one of the most fundamental tasks in machine learning and the second is one of the most promising applications of modern machine learning. This project aims at: 1) understanding the computational and statistical limits of DP stochastic optimization algorithms, 2) building a comprehensive theory for DP stochastic non-convex optimization, which provides a firm basis for developing new DP algorithms for modern machine learning, and 3) developing new, efficient algorithmic paradigms for DP federated learning that offer meaningful and provable utility guarantees, while taking into account the evolving nature of users’ data and their incentives to participate in collaborative learning. The outcomes of this research are expected to yield the next-generation privacy-preserving learning algorithms that can be implemented for widespread practical use. This career project includes educational and outreach activities such as developing new graduate courses on optimization and differential privacy, and organizing workshops to understand the threats to data privacy in modern 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.

尽管现代机器学习算法具有众多的社会效益，但它们对个人隐私构成了真实的威胁。差分隐私是一个数学框架，可以设计具有可证明隐私保证的学习算法。尽管最近在差分私有（DP）机器学习方面取得了进展，但我们目前对DP学习算法的基本特征的理解非常有限。这个职业项目提供了一个多方面的研究计划，解决DP机器学习两个重要领域的广泛基本问题：（i）随机优化和（ii）联邦学习。第一个是机器学习中最基本的任务之一，第二个是现代机器学习最有前途的应用之一。该项目旨在：1）理解DP随机优化算法的计算和统计限制，2）建立DP随机非凸优化的综合理论，为现代机器学习开发新的DP算法提供坚实的基础，以及3）为DP联邦学习开发新的，有效的算法范例，提供有意义的和可证明的效用保证，同时考虑到用户数据的不断变化的性质及其参与协作学习的动机。这项研究的成果预计将产生下一代隐私保护学习算法，可以实现广泛的实际应用。该职业项目包括教育和推广活动，例如开发关于优化和差异隐私的新研究生课程，以及组织研讨会以了解现代机器学习对数据隐私的威胁。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。